Droplet-based method and apparatus for composite single-cell nucleic acid analysis

ABSTRACT

The present invention generally relates to a combination of molecular barcoding and emulsion-based microfluidics to isolate, lyse, barcode, and prepare nucleic acids from individual cells in a high-throughput manner.

RELATED APPLICATIONS AND INCORPORATION BY REFERENCE

This application is a Continuation-in-Part of International Application Number PCT/US15/49178 filed on Sep. 9, 2015, which published as WO2016/040476 on Mar. 17, 2016 and claims benefit of and priority to US provisional patent application Ser. Nos. 62/048,227 filed Sep. 9, 2014; 62/146,642 filed Apr. 13, 2015.

FEDERAL FUNDING LEGEND

This invention was made with government support under Grant No. HG006193 awarded by the National Institutes of Health. The government has certain rights to the invention.

The foregoing applications, and all documents cited therein or during their prosecution (“appln cited documents”) and all documents cited or referenced in the appln cited documents, and all documents cited or referenced herein (“herein cited documents”), and all documents cited or referenced in herein cited documents, together with any manufacturer's instructions, descriptions, product specifications, and product sheets for any products mentioned herein or in any document incorporated by reference herein, are hereby incorporated herein by reference, and may be employed in the practice of the invention. More specifically, all referenced documents are incorporated by reference to the same extent as if each individual document was specifically and individually indicated to be incorporated by reference.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created Mar. 6, 2017 is named 4800992041_SL.txt and is 17.492 bytes in size.

FIELD OF INVENTION

The present invention generally relates to a combination of molecular barcoding and emulsion-based microfluidics to isolate, lyse, barcode, and prepare nucleic acids from individual cells in a high-throughput manner.

BACKGROUND OF THE INVENTION

Cells come in different types, sub-types and activity states, which Applicants classify based on their shape, location, function, or molecular profiles, such as the set of RNAs that they express. RNA profiling is in principle particularly informative, as cells express thousands of different RNAs. Approaches that measure for example the level of every type of RNA have until recently been applied to “homogenized” samples—in which the contents of all the cells are mixed together. This has greatly limited our ability to use such techniques to understand human tissue function and pathology, for example in the brain. In the past two years, new technologies have begun emerging to conduct such measurements in single cells, but they are not yet scalable to large numbers of cells, and are very costly. Here, Applicants develop a method to profile the RNA content of tens and hundreds of thousands of individual human cells, including from brain tissues, quickly and inexpensively. To do so, Applicants use special microfluidic devices to encapsulate each cell in an individual drop, associate the RNA of each cell with a ‘cell barcode’ unique to that cell/drop, measure the expression level of each RNA with sequencing, and then use the cell barcodes to determine which cell each RNA molecule came from. Applicants can use this approach to better understand almost any biological sample; it is particularly important for understanding samples from any complex tissue, for example the retina.

Performing studies that require data resolution at the single cell (or single molecule) level can be challenging or cost prohibitive under the best circumstances. Although techniques or instruments for single molecule or single cell analysis exist (e.g., digital polymerase chain reactions (PCR) or Fluidigm C1, respectively), none currently allows a scalable method for dynamically delivering reagents and/or appending molecular “information” to individual reactions such that a large population of reactions/assays can be processed and analyzed en masse while still maintaining the ability to partition results by individual reactions/assays.

Microfluidics involves micro-scale devices that handle small volumes of fluids. Because microfluidics may accurately and reproducibly control and dispense small fluid volumes, in particular volumes less than 1 μl, application of microfluidics provides significant cost-savings. The use of microfluidics technology reduces cycle times, shortens time-to-results, and increases throughput. Furthermore, incorporation of microfluidics technology enhances system integration and automation. Microfluidic reactions are generally conducted in microdroplets. The ability to conduct reactions in microdroplets depends on being able to merge different sample fluids and different microdroplets. See, e.g., US Patent Publication No. 20120219947.

Droplet microfluidics offers significant advantages for performing high-throughput screens and sensitive assays. Droplets allow sample volumes to be significantly reduced, leading to concomitant reductions in cost. Manipulation and measurement at kilohertz speeds enable up to 108 discrete biological entities (including, but not limited to, individual cells or organelles) to be screened in a single day. Compartmentalization in droplets increases assay sensitivity by increasing the effective concentration of rare species and decreasing the time required to reach detection thresholds. Droplet microfluidics combines these powerful features to enable currently inaccessible high-throughput screening applications, including single-cell and single-molecule assays. See, e.g., Guo et al., Lab Chip, 2012, 12, 2146-2155.

Citation or identification of any document in this application is not an admission that such document is available as prior art to the present invention.

SUMMARY OF THE INVENTION

The invention particularly relates to a combination of molecular barcoding and emulsion-based microfluidics to isolate, lyse, barcode, and prepare nucleic acids from individual cells in a high-throughput manner.

The invention provides a high-throughput single-cell RNA-Seq and/or targeted nucleic acid profiling (for example, sequencing, quantitative reverse transcription polymerase chain reaction, and the like) where the RNAs from different cells are tagged individually, allowing a single library to be created while retaining the cell identity of each read. A combination of molecular barcoding and emulsion-based microfluidics to isolate, lyse, barcode, and prepare nucleic acids from individual cells in high-throughput is used. Microfluidic devices (for example, fabricated in polydimethylsiloxane), sub-nanoliter reverse emulsion droplets. These droplets are used to co-encapsulate nucleic acids with a barcoded capture bead. Each bead, for example, is uniquely barcoded so that each drop and its contents are distinguishable. The nucleic acids may come from any source known in the art, such as for example, those which come from a single cell, a pair of cells, a cellular lysate, or a solution. The cell is lysed as it is encapsulated in the droplet. To load single cells and barcoded beads into these droplets with Poisson statistics, 100,000 to 10 million such beads are needed to barcode ˜10,000-100,000 cells.

The invention provides a method for creating a single-cell sequencing library comprising: merging one uniquely barcoded mRNA capture microbead with a single-cell in an emulsion droplet having a diameter of 75-125 μm; lysing the cell to make its RNA accessible for capturing by hybridization onto RNA capture microbead; performing a reverse transcription either inside or outside the emulsion droplet to convert the cell's mRNA to a first strand cDNA that is covalently linked to the mRNA capture microbead; pooling the cDNA-attached microbeads from all cells; and preparing and sequencing a single composite RNA-Seq library.

The invention provides a method for preparing uniquely barcoded mRNA capture microbeads, which has a unique barcode and diameter suitable for microfluidic devices comprising: 1) performing reverse phosphoramidite synthesis on the surface of the bead in a pool-and-split fashion, such that in each cycle of synthesis the beads are split into four reactions with one of the four canonical nucleotides (T, C, G, or A) or unique oligonucleotides of length two or more bases; 2) repeating this process a large number of times, at least two, and optimally more than twelve, such that, in the latter, there are more than 16 million unique barcodes on the surface of each bead in the pool. (See www.ncbi.nlm.nih.gov/pmc/articles/PMC206447)

Generally, the invention provides a method for preparing a large number of beads, particles, microbeads, nanoparticles, or the like with unique nucleic acid barcodes comprising performing polynucleotide synthesis on the surface of the beads in a pool-and-split fashion such that in each cycle of synthesis the beads are split into subsets that are subjected to different chemical reactions; and then repeating this split-pool process in two or more cycles, to produce a combinatorially large number of distinct nucleic acid barcodes. Invention further provides performing a polynucleotide synthesis wherein the synthesis may be any type of synthesis known to one of skill in the art for “building” polynucleotide sequences in a step-wise fashion. Examples include, but are not limited to, reverse direction synthesis with phosphoramidite chemistry or forward direction synthesis with phosphoramidite chemistry. Previous and well-known methods synthesize the oligonucleotides separately then “glue” the entire desired sequence onto the bead enzymatically. Applicants present a complexed bead and a novel process for producing these beads where nucleotides are chemically built onto the bead material in a high-throughput manner. Moreover, Applicants generally describe delivering a “packet” of beads which allows one to deliver millions of sequences into separate compartments and then screen all at once.

The invention further provides an apparatus for creating a single-cell sequencing library via a microfluidic system, comprising: a oil-surfactant inlet comprising a filter and a carrier fluid channel, wherein said carrier fluid channel further comprises a resistor; an inlet for an analyte comprising a filter and a carrier fluid channel, wherein said carrier fluid channel further comprises a resistor; an inlet for mRNA capture microbeads and lysis reagent comprising a filter and a carrier fluid channel, wherein said carrier fluid channel further comprises a resistor; said carrier fluid channels have a carrier fluid flowing therein at an adjustable or predetermined flow rate; wherein each said carrier fluid channels merge at a junction; and said junction being connected to a mixer, which contains an outlet for drops.

Accordingly, it is an object of the invention not to encompass within the invention any previously known product, process of making the product, or method of using the product such that Applicants reserve the right and hereby disclose a disclaimer of any previously known product, process, or method. It is further noted that the invention does not intend to encompass within the scope of the invention any product, process, or making of the product or method of using the product, which does not meet the written description and enablement requirements of the USPTO (35 U.S.C. § 112, first paragraph) or the EPO (Article 83 of the EPC), such that Applicants reserve the right and hereby disclose a disclaimer of any previously described product, process of making the product, or method of using the product.

It is noted that in this disclosure and particularly in the claims and/or paragraphs, terms such as “comprises”, “comprised”, “comprising” and the like can have the meaning attributed to it in U.S. Patent law; e.g., they can mean “includes”, “included”, “including”, and the like; and that terms such as “consisting essentially of” and “consists essentially of” have the meaning ascribed to them in U.S. Patent law, e.g., they allow for elements not explicitly recited, but exclude elements that are found in the prior art or that affect a basic or novel characteristic of the invention.

These and other embodiments are disclosed or are obvious from and encompassed by, the following Detailed Description.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description, given by way of example, but not intended to limit the invention solely to the specific embodiments described, may best be understood in conjunction with the accompanying drawings.

FIG. 1 illustrates a microfluidic droplet according to an exemplary disclosed embodiment. Figure discloses “AAAAAAAAAAAA” and “TTTTTTTTTTTT” as SEQ ID NOS: 17 and 29, respectively.

FIGS. 2A and 2B illustrates an embodiment of the present invention which builds barcodes by split-and-pool synthesis on beads using single bases and a final oligo-dT tail for mRNA capture.

FIGS. 3A-3D illustrate cell barcode sequences approaching the theoretical level of complexity. FIG. 3D discloses SEQ ID NOS: 18-37, respectively, in order of appearance.

FIG. 4 . Microfluidic device illustrating co-encapsulation of cells in PBS injected (once).

FIG. 5 . Schematic illustration of microfluidic device.

FIG. 6 . illustrates sorted drops of interest using the drop-seq method generated from the microfluidic device.

FIGS. 7 A-D illustrate molecular barcoding of cellular transcriptomes in droplets.

FIGS. 8 A-D illustrate extraction and processing of single-cell transcriptomes by Drop-Seq. FIG. 8D discloses left column sequences as SEQ ID NOS: 38-50 and right column sequences as SEQ ID NOS: 51-63, respectively, in order of appearance.

FIG. 9 A-G illustrate critical evaluation of Drop-Seq using species-mixing experiments.

FIG. 10 A-C illustrate cell-cycle analysis of HEK and 3T3 cells analyzed by Drop-Seq.

FIG. 11 A-F illustrate Ab initio reconstruction of retinal cell types from 44,808 single-cell transcription profiles prepared by Drop-Seq.

FIG. 12 A-I Finer-scale expression distinctions among amacrine cells, cones and retinal ganglion cells.

FIG. 13 A-C illustrate Ab initio reconstruction of human bone marrow cell types from 471 single-cell transcription profiles prepared by Drop-Seq.

FIG. 14 A-C illustrate an assessment of the properties of barcoded primers on the surface of microparticles (beads).

FIG. 15 A-E illustrate device design and dissection of technical contributions to single-cell impurities in Drop-Seq library preparations.

FIG. 16 A-F illustrates specificity and sensitivity as a function of sequencing coverage, evaluated by down-sampling low-depth and high-depth species-mixed (HEK/293T) Drop-Seq libraries prepared at a concentration of 50 cells/μl. (A,B) Analysis of specificity.

FIG. 17 A-F illustrates estimation of Drop-Seq expression bias and capture efficiency.

FIG. 18 illustrates plots of principal components 1-32 of the 44,808 retinal cell STAMPs used in analysis.

FIG. 19 illustrates violin plots showing expression of selected marker genes in the 39 retinal cell clusters generated by unsupervised analysis of single-cell gene expression.

FIG. 20 shows the fraction of each cluster composed of cells deriving from one of the seven replicates that composed the full 44,808-cell data set.

FIG. 21 illustrates a schematic representation of Drop-Seq setup.

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description is of example embodiments of the presently claimed invention with references to the accompanying drawings. Such description is intended to be illustrative and not limiting with respect to the scope of the present invention. Such embodiments are described in sufficient detail to enable one of ordinary skill in the art to practice the subject invention, and it will be understood that other embodiments may be practiced with some variations without departing from the spirit or scope of the subject invention.

The invention provides a nucleotide- or oligonucleotide-adorned bead wherein said bead comprises: a linker; an identical sequence for use as a sequencing priming site; a uniform or near-uniform nucleotide or oligonucleotide sequence; a Unique Molecular Identifier which differs for each priming site; optionally an oligonucleotide redundant sequence for capturing polyadenylated mRNAs and priming reverse transcription; and optionally at least one other oligonucleotide barcode which provides an additional substrate for identification.

In an embodiment of the invention, the nucleotide or oligonucleotide sequences on the surface of the bead is a molecular barcode. In an further embodiment the barcode ranges from 4 to 1000 nucleotides in length. In another embodiment, the oligonucleotide sequence for capturing polyadenylated mRNAs and priming reverse transcription is an oligo dT sequence.

In an embodiment of the invention, the linker is a non-cleavable, straight-chain polymer. In another embodiment, the linker is a chemically-cleavable, straight-chain polymer. In a further embodiment, the linker is a non-cleavable, optionally substituted hydrocarbon polymer. In another embodiment, the linker is a photolabile optionally substituted hydrocarbon polymer. In another embodiment, the linker is a polyethylene glycol. In an embodiment, the linker is a PEG-C₃ to PEG-₂₄.

The invention provides a mixture comprising a plurality of nucleotide- or oligonucleotide-adorned beads, wherein said beads comprises: a linker; an identical sequence for use as a sequencing priming site; a uniform or near-uniform nucleotide or oligonucleotide sequence; a Unique Molecular Identifier which differs for each priming site; an oligonucleotide redundant sequence for capturing polyadenylated mRNAs and priming reverse transcription; and optionally at least one additional oligonucleotide sequences, which provide substrates for downstream molecular-biological reactions; wherein the uniform or near-uniform nucleotide or oligonucleotide sequence is the same across all the priming sites on any one bead, but varies among the oligonucleotides on an individual bead.

In an embodiment of the invention, the nucleotide or oligonucleotide sequence on the surface of the bead is a molecular barcode. In an further embodiment the barcode ranges from 4 to 1000 nucleotides in length. In another embodiment, the oligonucleotide sequence for capturing polyadenylated mRNAs and priming reverse transcription is an oligo dT sequence.

In an embodiment of the invention, the mixture comprises at least one oligonucleotide sequences, which provide for substrates for downstream molecular-biological reactions. In another embodiment, the downstream molecular biological reactions are for reverse transcription of mature mRNAs; capturing specific portions of the transcriptome, priming for DNA polymerases and/or similar enzymes; or priming throughout the transcriptome or genome. In an embodiment of the invention, the additional oligonucleotide sequence comprises a oligo-dT sequence. In another embodiment of the invention, the additional oligonucleotide sequence comprises a primer sequence. In an embodiment of the invention, the additional oligonucleotide sequence comprises a oligo-dT sequence and a primer sequence.

The invention provides an error-correcting barcode bead wherein said bead comprises: a linker; an identical sequence for use as a sequencing priming site; a uniform or near-uniform nucleotide or oligonucleotide sequence which comprises at least a nucleotide base duplicate; a Unique Molecular Identifier which differs for each priming site; and an oligonucleotide redundant for capturing polyadenylated mRNAs and priming reverse transcription.

In an embodiment of the invention, the error-correcting barcode beads fail to hybridize to the mRNA thereby failing to undergo reverse transcription.

The invention also provides a kit which comprises a mixture of oligonucleotide bound beads and self-correcting barcode beads.

The invention provides a method for creating a single-cell sequencing library comprising: merging one uniquely barcoded RNA capture microbead with a single-cell in an emulsion droplet having a diameter from 50 μm to 210 μm; lysing the cell thereby capturing the RNA on the RNA capture microbead; breaking droplets and pooling beads in solution; performing a reverse transcription reaction to convert the cells' RNA to first strand cDNA that is covalently linked to the RNA capture microbead; or conversely reverse transcribing within droplets and thereafter breaking droplets and collecting cDNA-attached beads; preparing and sequencing a single composite RNA-Seq library, containing cell barcodes that record the cell-of-origin of each RNA, and molecular barcodes that distinguish among RNAs from the same cell.

In an embodiment the diameter of the emulsion droplet is between 50-210 μm. In a further embodiment, the method wherein the diameter of the mRNA capture microbeads is from 10 μm to 95 μm. In a further embodiment the diameter of the emulsion droplet is 125 μm.

The invention provides a method for preparing a plurality of beads with unique nucleic acid sequence comprising: performing polynucleotide synthesis on the surface of the plurality of beads in a pool-and-split process, such that in each cycle of synthesis the beads are split into a plurality of subsets wherein each subset is subjected to different chemical reactions; repeating the pool-and-split process from anywhere from 2 cycles to 200 cycles.

In an embodiment of the invention the polynucleotide synthesis is phosphoramidite synthesis. In another embodiment of the invention the polynucleotide synthesis is reverse direction phosphoramidite chemistry. In an embodiment of the invention, each subset is subjected to a different nucleotide. In another embodiment, each subset is subjected to a different canonical nucleotide. In an embodiment of the invention the method is repeated three, four, or twelve times.

In an embodiment the covalent bond is polyethylene glycol. In another embodiment the diameter of the mRNA capture microbeads is from 10 μm to 95 μm. In an embodiment, wherein the multiple steps is twelve steps.

In a further embodiment the method further comprises a method for preparing uniquely barcoded mRNA capture microbeads, which has a unique barcode and diameter suitable for microfluidic devices comprising: 1) performing reverse phosphoramidite synthesis on the surface of the bead in a pool-and-split fashion, such that in each cycle of synthesis the beads are split into four reactions with one of the four canonical nucleotides (T, C, G, or A); 2) repeating this process a large number of times, at least six, and optimally more than twelve, such that, in the latter, there are more than 16 million unique barcodes on the surface of each bead in the pool.

In an embodiment, the diameter of the mRNA capture microbeads is from 10 μm to 95 μm.

The invention provides a method for simultaneously preparing a plurality of nucleotide- or oligonucleotide-adorned beads wherein a uniform, near-uniform, or patterned nucleotide or oligonucleotide sequence is synthesized upon any individual bead while vast numbers of different nucleotide or oligonucleotide sequences are simultaneously synthesized on different beads, comprising: forming a mixture comprising a plurality of beads; separating the beads into subsets; extending the nucleotide or oligonucleotide sequence on the surface of the beads by adding an individual nucleotide via chemical synthesis; pooling the subsets of beads in

(c) into a single common pool; repeating steps (b), (c) and (d) multiple times to produce a combinatorially a thousand or more nucleotide or oligonucleotide sequences; and collecting the nucleotide- or oligonucleotide-adorned beads.

In an embodiment of the invention, the nucleotide or oligonucleotide sequence on the surface of the bead is a molecular barcode. In a further embodiment, the pool-and-split synthesis steps occur every 2-10 cycles, rather than every cycle.

In an embodiment of the invention, the barcode contains built-in error correction. In another embodiment, the barcode ranges from 4 to 1000 nucleotides in length. In embodiment of the invention the polynucleotide synthesis is phosphoramidite synthesis. In a further embodiment, the polynucleotide synthesis is reverse direction phosphoramidite chemistry. In an embodiment of the invention each subset is subjected to a different nucleotide. In a further embodiment, one or more subsets receive a cocktail of two nucleotides. In an embodiment, each subset is subjected to a different canonical nucleotide.

The method provided by the invention contemplates a variety of embodiments wherein the bead is a microbead, a nanoparticle, or a macrobead. Similarly, the invention contemplates that the oligonucleotide sequence is a dinucleotide or trinucleotide.

The invention provides a method for simultaneously preparing a thousand or more nucleotide- or oligonucleotide-adorned beads wherein a uniform or near-uniform nucleotide or oligonucleotide sequence is synthesized upon any individual bead while a plurality of different nucleotide or oligonucleotide sequences are simultaneously synthesized on different beads, comprising: forming a mixture comprising a plurality of beads; separating the beads into subsets; extending the nucleotide or oligonucleotide sequence on the surface of the beads by adding an individual nucleotide via chemical synthesis; pooling the subsets of beads in (c) into a single common pool; repeating steps (b), (c) and (d) multiple times to produce a combinatorially large number of nucleotide or oligonucleotide sequences; and collecting the nucleotide- or oligonucleotide-adorned beads; performing polynucleotide synthesis on the surface of the plurality of beads in a pool-and-split synthesis, such that in each cycle of synthesis the beads are split into a plurality of subsets wherein each subset is subjected to different chemical reactions; repeating the pool-and-split synthesis multiple times.

In an embodiment of the invention, the nucleotide or oligonucleotide sequence on the surface of the bead is a molecular barcode. In an embodiment, the pool-and-split synthesis steps occur every 2 to 10 cycles, rather than every cycle. In an embodiment, the generated barcode contains built-in error correction. In another embodiment, the barcode ranges from 4 to 1000 nucleotides in length. In embodiment of the invention the polynucleotide synthesis is phosphoramidite synthesis. In a further embodiment, the polynucleotide synthesis is reverse direction phosphoramidite chemistry. In an embodiment of the invention each subset is subjected to a different nucleotide. In a further embodiment, one or more subsets receive a cocktail of two nucleotides. In an embodiment, each subset is subjected to a different canonical nucleotide.

The method provided by the invention contemplates a variety of embodiments wherein the bead is a microbead, a nanoparticle, or a macrobead. Similarly, the invention contemplates that the oligonucleotide sequence is a dinucleotide or trinucleotide.

The invention further provides an apparatus for creating a composite single-cell sequencing library via a microfluidic system, comprising: an oil-surfactant inlet comprising a filter and two carrier fluid channels, wherein said carrier fluid channel further comprises a resistor; an inlet for an analyte comprising a filter and two carrier fluid channels, wherein said carrier fluid channel further comprises a resistor; an inlet for mRNA capture microbeads and lysis reagent comprising a carrier fluid channel; said carrier fluid channels have a carrier fluid flowing therein at an adjustable and predetermined flow rate; wherein each said carrier fluid channels merge at a junction; and said junction being connected to a constriction for droplet pinch-off followed by a mixer, which connects to an outlet for drops.

In an embodiment of the apparatus, the analyte comprises a chemical reagent, a genetically perturbed cell, a protein, a drug, an antibody, an enzyme, a nucleic acid, an organelle like the mitochondrion or nucleus, a cell or any combination thereof. In an embodiment of the apparatus the analyte is a cell. In a further embodiment, the analyte is a mammalian cell. In another embodiment, the analyte of the apparatus is complex tissue. In a further embodiment, the cell is a brain cell. In an embodiment of the invention, the cell is a retina cell. In another embodiment the cell is a human bone marrow cell. In an embodiment, the cell is a host-pathogen cell.

In an embodiment of the apparatus the lysis reagent comprises an anionic surfactant such as sodium lauroyl sarcosinate, or a chaotropic salt such as guanidinium thiocyanate. In an embodiment of the apparatus the filter is consists of square PDMS posts; the filter on the cell channel consists of such posts with sides ranging between 125-135 μm with a separation of 70-100 mm between the posts. The filter on the oil-surfactant inlet comprises square posts of two sizes; one with sides ranging between 75-100 μm and a separation of 25-30 μm between them and the other with sides ranging between 40-50 μm and a separation of 10-15 μm. In an embodiment of the apparatus the resistor is serpentine having a length of 7000-9000 μm, width of 50-75 μm and depth of 100-150 mm. In an embodiment of the apparatus the channels have a length of 8000-12,000 μm for oil-surfactant inlet, 5000-7000 for analyte (cell) inlet, and 900-1200 μm for the inlet for microbead and lysis agent. All channels have a width of 125-250 mm, and depth of 100-150 mm. In another embodiment, the width of the cell channel is 125-250 μm and the depth is 100-150 μm. In an embodiment of the apparatus the mixer has a length of 7000-9000 μm, and a width of 110-140 μm with 35-45° zig-zigs every 150 μm. In an embodiment, the width of the mixer is 125 μm. In an embodiment of the apparatus the oil-surfactant is PEG Block Polymer, such as BIORAD™ QX200 Droplet Generation Oil. In an embodiment of the apparatus the carrier fluid is water-glycerol mixture.

A mixture comprising a plurality of microbeads adorned with combinations of the following elements: bead-specific oligonucleotide barcodes created by the methods provided; additional oligonucleotide barcode sequences which vary among the oligonucleotides on an individual bead and can therefore be used to differentiate or help identify those individual oligonucleotide molecules; additional oligonucleotide sequences that create substrates for downstream molecular-biological reactions, such as oligo-dT (for reverse transcription of mature mRNAs), specific sequences (for capturing specific portions of the transcriptome, or priming for DNA polymerases and similar enzymes), or random sequences (for priming throughout the transcriptome or genome). In an embodiment, the individual oligonucleotide molecules on the surface of any individual microbead contain all three of these elements, and the third element includes both oligo-dT and a primer sequence.

In another embodiment, a mixture comprising a plurality of microbeads, wherein said microbeads comprise the following elements: at least one bead-specific oligonucleotide barcode obtainable by the process outlined; at least one additional identifier oligonucleotide barcode sequence, which varies among the oligonucleotides on an individual bead, and thereby assisting in the identification and of the bead specific oligonucleotide molecules; optionally at least one additional oligonucleotide sequences, which provide substrates for downstream molecular-biological reactions. In another embodiment the mixture comprises at least one oligonucleotide sequences, which provide for substrates for downstream molecular-biological reactions. In a further embodiment the downstream molecular biological reactions are for reverse transcription of mature mRNAs; capturing specific portions of the transcriptome, priming for DNA polymerases and/or similar enzymes; or priming throughout the transcriptome or genome. In a further embodiment the mixture the additional oligonucleotide sequence comprising a oligo-dT sequence. In another embodiment the mixture further comprises the additional oligonucleotide sequence comprises a primer sequence. In another embodiment the mixture further comprises the additional oligonucleotide sequence comprising a oligo-dT sequence and a primer sequence.

Examples of the labeling substance which may be employed include labeling substances known to those skilled in the art, such as fluorescent dyes, enzymes, coenzymes, chemiluminescent substances, and radioactive substances. Specific examples include radioisotopes (e.g., ³²P, ¹C, ¹²⁵, ³H, and ¹³¹I), fluorescein, rhodamine, dansyl chloride, umbelliferone, luciferase, peroxidase, alkaline phosphatase, β-galactosidase, β-glucosidase, horseradish peroxidase, glucoamylase, lysozyme, saccharide oxidase, microperoxidase, biotin, and ruthenium. In the case where biotin is employed as a labeling substance, preferably, after addition of a biotin-labeled antibody, streptavidin bound to an enzyme (e.g., peroxidase) is further added.

Advantageously, the label is a fluorescent label. Examples of fluorescent labels include, but are not limited to, Atto dyes, 4-acetamido-4′-isothiocyanatostilbene-2,2′disulfonic acid; acridine and derivatives: acridine, acridine isothiocyanate; 5-(2′-aminoethyl)aminonaphthalene-1-sulfonic acid (EDANS); 4-amino-N-[3-vinylsulfonyl)phenyl]naphthalimide-3,5 disulfonate; N-(4-anilino-1-naphthyl)maleimide; anthranilamide; BODIPY; Brilliant Yellow; coumarin and derivatives; coumarin, 7-amino-4-methylcoumarin (AMC, Coumarin 120), 7-amino-4- trifluoromethylcoumarin (Coumaran 151); cyanine dyes; cyanosine; 4′,6- diamidino-2-phenylindole (DAPI); 5′5″-dibromopyrogallol-sulfonphthalein (Bromopyrogallol Red); 7-diethylamino-3-(4′-isothiocyanatophenyl)-4-methylcoumarin; diethylenetriamine pentaacetate; 4,4′-diisothiocyanatodihydro-stilbene-2,2′-disulfonic acid; 4,4′-diisothiocyanatostilbene-2,2′-disulfonic acid; 5-[dimethylamino]naphthalene-1-sulfonyl chloride (DNS, dansylchloride); 4-dimethylaminophenylazophenyl-4′-isothiocyanate (DABITC); eosin and derivatives; eosin, eosin isothiocyanate, erythrosin and derivatives; erythrosin B, erythrosin, isothiocyanate; ethidium; fluorescein and derivatives; 5-carboxyfluorescein (FAM), 5-(4,6-dichlorotriazin-2-yl)aminofluorescein (DTAF), 2′,7′-dimethoxy-4′5′-dichloro-6-carboxyfluorescein, fluorescein, fluorescein isothiocyanate, QFITC, (XRITC); fluorescamine; IR144; IR1446; Malachite Green isothiocyanate; 4-methylumbelliferoneortho cresolphthalein; nitrotyrosine; pararosaniline; Phenol Red; B-phycoerythrin; o-phthaldialdehyde; pyrene and derivatives: pyrene, pyrene butyrate, succinimidyl 1-pyrene; butyrate quantum dots; Reactive Red 4 (Cibacron™ Brilliant Red 3B-A) rhodamine and derivatives: 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), lissamine rhodamine B sulfonyl chloride rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, sulforhodamine B, sulforhodamine 101, sulfonyl chloride derivative of sulforhodamine 101 (Texas Red); N,N,N′,N′ tetramethyl-6-carboxyrhodamine (TAMRA); tetramethyl rhodamine; tetramethyl rhodamine isothiocyanate (TRITC); riboflavin; rosolic acid; terbium chelate derivatives; Cy3; Cy5; Cy5.5; Cy7; IRD 700; IRD 800; La Jolta Blue; phthalo cyanine; and naphthalo cyanine.

The fluorescent label may be a fluorescent protein, such as blue fluorescent protein, cyan fluorescent protein, green fluorescent protein, red fluorescent protein, yellow fluorescent protein or any photoconvertible protein. Colormetric labeling, bioluminescent labeling and/or chemiluminescent labeling may further accomplish labeling. Labeling further may include energy transfer between molecules in the hybridization complex by perturbation analysis, quenching, or electron transport between donor and acceptor molecules, the latter of which may be facilitated by double stranded match hybridization complexes. The fluorescent label may be a perylene or a terylene. In the alternative, the fluorescent label may be a fluorescent bar code.

In an advantageous embodiment, the label may be light sensitive, wherein the label is light-activated and/or light cleaves the one or more linkers to release the molecular cargo. The light-activated molecular cargo may be a major light-harvesting complex (LHCII). In another embodiment, the fluorescent label may induce free radical formation.

In an advantageous embodiment, agents may be uniquely labeled in a dynamic manner (see, e.g., U.S. provisional patent application Ser. No. 61/703,884 filed Sep. 21, 2012). The unique labels are, at least in part, nucleic acid in nature, and may be generated by sequentially attaching two or more detectable oligonucleotide tags to each other and each unique label may be associated with a separate agent. A detectable oligonucleotide tag may be an oligonucleotide that may be detected by sequencing of its nucleotide sequence and/or by detecting non-nucleic acid detectable moieties to which it may be attached.

The oligonucleotide tags may be detectable by virtue of their nucleotide sequence, or by virtue of a non-nucleic acid detectable moiety that is attached to the oligonucleotide such as but not limited to a fluorophore, or by virtue of a combination of their nucleotide sequence and the nonnucleic acid detectable moiety.

In some embodiments, a detectable oligonucleotide tag may comprise one or more nonoligonucleotide detectable moieties. Examples of detectable moieties may include, but are not limited to, fluorophores, microparticles including quantum dots (Empodocles, et al., Nature 399:126-130, 1999), gold nanoparticles (Reichert et al., Anal. Chem. 72:6025-6029, 2000), microbeads (Lacoste et al., Proc. Natl. Acad. Sci. USA 97(17):9461-9466, 2000), biotin, DNP (dinitrophenyl), fucose, digoxigenin, haptens, and other detectable moieties known to those skilled in the art. In some embodiments, the detectable moieties may be quantum dots. Methods for detecting such moieties are described herein and/or are known in the art.

Thus, detectable oligonucleotide tags may be, but are not limited to, oligonucleotides which may comprise unique nucleotide sequences, oligonucleotides which may comprise detectable moieties, and oligonucleotides which may comprise both unique nucleotide sequences and detectable moieties.

A unique label may be produced by sequentially attaching two or more detectable oligonucleotide tags to each other. The detectable tags may be present or provided in a plurality of detectable tags. The same or a different plurality of tags may be used as the source of each detectable tag may be part of a unique label. In other words, a plurality of tags may be subdivided into subsets and single subsets may be used as the source for each tag.

In some embodiments, one or more other species may be associated with the tags. In particular, nucleic acids released by a lysed cell may be ligated to one or more tags. These may include, for example, chromosomal DNA, RNA transcripts, tRNA, mRNA, mitochondrial DNA, or the like. Such nucleic acids may be sequenced, in addition to sequencing the tags themselves, which may yield information about the nucleic acid profile of the cells, which can be associated with the tags, or the conditions that the corresponding droplet or cell was exposed to.

The invention described herein enables high throughput and high resolution delivery of reagents to individual emulsion droplets that may contain cells, organelles, nucleic acids, proteins, etc. through the use of monodisperse aqueous droplets that are generated by a microfluidic device as a water-in-oil emulsion. The droplets are carried in a flowing oil phase and stabilized by a surfactant. In one aspect single cells or single organelles or single molecules (proteins, RNA, DNA) are encapsulated into uniform droplets from an aqueous solution/dispersion. In a related aspect, multiple cells or multiple molecules may take the place of single cells or single molecules. The aqueous droplets of volume ranging from 1 pL to 10 nL

work as individual reactors. Disclosed embodiments provide thousands of single cells in droplets which can be processed and analyzed in a single run.

To utilize microdroplets for rapid large-scale chemical screening or complex biological library identification, different species of microdroplets, each containing the specific chemical compounds or biological probes cells or molecular barcodes of interest, have to be generated and combined at the preferred conditions, e.g., mixing ratio, concentration, and order of combination.

Each species of droplet is introduced at a confluence point in a main microfluidic channel from separate inlet microfluidic channels. Preferably, droplet volumes are chosen by design such that one species is larger than others and moves at a different speed, usually slower than the other species, in the carrier fluid, as disclosed in U.S. Publication No. US 2007/0195127 and International Publication No. WO 2007/089541, each of which are incorporated herein by reference in their entirety. The channel width and length is selected such that faster species of droplets catch up to the slowest species. Size constraints of the channel prevent the faster moving droplets from passing the slower moving droplets resulting in a train of droplets entering a merge zone. Multi-step chemical reactions, biochemical reactions, or assay detection chemistries often require a fixed reaction time before species of different type are added to a reaction. Multi-step reactions are achieved by repeating the process multiple times with a second, third or more confluence points each with a separate merge point. Highly efficient and precise reactions and analysis of reactions are achieved when the frequencies of droplets from the inlet channels are matched to an optimized ratio and the volumes of the species are matched to provide optimized reaction conditions in the combined droplets.

Fluidic droplets may be screened or sorted within a fluidic system of the invention by altering the flow of the liquid containing the droplets. For instance, in one set of embodiments, a fluidic droplet may be steered or sorted by directing the liquid surrounding the fluidic droplet into a first channel, a second channel, etc. In another set of embodiments, pressure within a fluidic system, for example, within different channels or within different portions of a channel, can be controlled to direct the flow of fluidic droplets. For example, a droplet can be directed toward a channel junction including multiple options for further direction of flow (e.g., directed toward a branch, or fork, in a channel defining optional downstream flow channels). Pressure within one or more of the optional downstream flow channels can be controlled to direct the droplet selectively into one of the channels, and changes in pressure can be effected on the order of the time required for successive droplets to reach the junction, such that the downstream flow path of each successive droplet can be independently controlled. In one arrangement, the expansion and/or contraction of liquid reservoirs may be used to steer or sort a fluidic droplet into a channel, e.g., by causing directed movement of the liquid containing the fluidic droplet. In another embodiment, the expansion and/or contraction of the liquid reservoir may be combined with other flow-controlling devices and methods, e.g., as described herein. Non-limiting examples of devices able to cause the expansion and/or contraction of a liquid reservoir include pistons.

Key elements for using microfluidic channels to process droplets include: (1) producing droplet of the correct volume, (2) producing droplets at the correct frequency and (3) bringing together a first stream of sample droplets with a second stream of sample droplets in such a way that the frequency of the first stream of sample droplets matches the frequency of the second stream of sample droplets. Preferably, bringing together a stream of sample droplets with a stream of premade library droplets in such a way that the frequency of the library droplets matches the frequency of the sample droplets.

Methods for producing droplets of a uniform volume at a regular frequency are well known in the art. One method is to generate droplets using hydrodynamic focusing of a dispersed phase fluid and immiscible carrier fluid, such as disclosed in U.S. Publication No. US 2005/0172476 and International Publication No. WO 2004/002627. It is desirable for one of the species introduced at the confluence to be a pre-made library of droplets where the library contains a plurality of reaction conditions, e.g., a library may contain plurality of different compounds at a range of concentrations encapsulated as separate library elements for screening their effect on cells or enzymes, alternatively a library could be composed of a plurality of different primer pairs encapsulated as different library elements for targeted amplification of a collection of loci, alternatively a library could contain a plurality of different antibody species encapsulated as different library elements to perform a plurality of binding assays. The introduction of a library of reaction conditions onto a substrate is achieved by pushing a premade collection of library droplets out of a vial with a drive fluid. The drive fluid is a continuous fluid. The drive fluid may comprise the same substance as the carrier fluid (e.g., a fluorocarbon oil). For example, if a library consists of ten pico-liter droplets is driven into an inlet channel on a microfluidic substrate with a drive fluid at a rate of 10,000 pico-liters per second, then nominally the frequency at which the droplets are expected to enter the confluence point is 1000 per second. However, in practice droplets pack with oil between them that slowly drains. Over time the carrier fluid drains from the library droplets and the number density of the droplets (number/mL) increases. Hence, a simple fixed rate of infusion for the drive fluid does not provide a uniform rate of introduction of the droplets into the microfluidic channel in the substrate. Moreover, library-to-library variations in the mean library droplet volume result in a shift in the frequency of droplet introduction at the confluence point. Thus, the lack of uniformity of droplets that results from sample variation and oil drainage provides another problem to be solved. For example if the nominal droplet volume is expected to be 10 pico-liters in the library, but varies from 9 to 11 pico-liters from library-to-library then a 10,000 pico-liter/second infusion rate will nominally produce a range in frequencies from 900 to 1,100 droplet per second. In short, sample to sample variation in the composition of dispersed phase for droplets made on chip, a tendency for the number density of library droplets to increase over time and library-to-library variations in mean droplet volume severely limit the extent to which frequencies of droplets may be reliably matched at a confluence by simply using fixed infusion rates. In addition, these limitations also have an impact on the extent to which volumes may be reproducibly combined. Combined with typical variations in pump flow rate precision and variations in channel dimensions, systems are severely limited without a means to compensate on a run-to-run basis. The foregoing facts not only illustrate a problem to be solved, but also demonstrate a need for a method of instantaneous regulation of microfluidic control over microdroplets within a microfluidic channel.

Combinations of surfactant(s) and oils must be developed to facilitate generation, storage, and manipulation of droplets to maintain the unique chemical/biochemical/biological environment within each droplet of a diverse library. Therefore, the surfactant and oil combination must (1) stabilize droplets against uncontrolled coalescence during the drop forming process and subsequent collection and storage, (2) minimize transport of any droplet contents to the oil phase and/or between droplets, and (3) maintain chemical and biological inertness with contents of each droplet (e.g., no adsorption or reaction of encapsulated contents at the oil-water interface, and no adverse effects on biological or chemical constituents in the droplets). In addition to the requirements on the droplet library function and stability, the surfactant-in-oil solution must be coupled with the fluid physics and materials associated with the platform. Specifically, the oil solution must not swell, dissolve, or degrade the materials used to construct the microfluidic chip, and the physical properties of the oil (e.g., viscosity, boiling point, etc.) must be suited for the flow and operating conditions of the platform.

Droplets formed in oil without surfactant are not stable to permit coalescence, so surfactants must be dissolved in the oil that is used as the continuous phase for the emulsion library. Surfactant molecules are amphiphilic—part of the molecule is oil soluble, and part of the molecule is water soluble. When a water-oil interface is formed at the nozzle of a microfluidic chip for example in the inlet module described herein, surfactant molecules that are dissolved in the oil phase adsorb to the interface. The hydrophilic portion of the molecule resides inside the droplet and the fluorophilic portion of the molecule decorates the exterior of the droplet. The surface tension of a droplet is reduced when the interface is populated with surfactant, so the stability of an emulsion is improved. In addition to stabilizing the droplets against coalescence, the surfactant should be inert to the contents of each droplet and the surfactant should not promote transport of encapsulated components to the oil or other droplets.

A droplet library may be made up of a number of library elements that are pooled together in a single collection (see, e.g., US Patent Publication No. 2010002241). Libraries may vary in complexity from a single library element to 1015 library elements or more. Each library element may be one or more given components at a fixed concentration. The element may be, but is not limited to, cells, organelles, virus, bacteria, yeast, beads, amino acids, proteins, polypeptides, nucleic acids, polynucleotides or small molecule chemical compounds. The element may contain an identifier such as a label. The terms “droplet library” or “droplet libraries” are also referred to herein as an “emulsion library” or “emulsion libraries.” These terms are used interchangeably throughout the specification.

A cell library element may include, but is not limited to, hybridomas, B-cells, primary cells, cultured cell lines, cancer cells, stem cells, cells obtained from tissue (e.g., retinal or human bone marrow), peripheral blood mononuclear cell, or any other cell type. Cellular library elements are prepared by encapsulating a number of cells from one to hundreds of thousands in individual droplets. The number of cells encapsulated is usually given by Poisson statistics from the number density of cells and volume of the droplet. However, in some cases the number deviates from Poisson statistics as described in Edd et al., “Controlled encapsulation of single-cells into monodisperse picolitre drops.” Lab Chip, 8(8): 1262-1264, 2008. The discrete nature of cells allows for libraries to be prepared in mass with a plurality of cellular variants all present in a single starting media and then that media is broken up into individual droplet capsules that contain at most one cell. These individual droplets capsules are then combined or pooled to form a library consisting of unique library elements. Cell division subsequent to, or in some embodiments following, encapsulation produces a clonal library element.

A variety of analytes may be contemplated for use with the foregoing Drop-Sequencing methods. Examples of cells which are contemplated are mammalian cells, however the invention contemplates a method for profiling host-pathogen cells. To characterize the expression of host-pathogen interactions it is important to grow the host and pathogen in the same cell without multiple opportunities of pathogen infection.

A bead based library element may contain one or more beads, of a given type and may also contain other reagents, such as antibodies, enzymes or other proteins. In the case where all library elements contain different types of beads, but the same surrounding media, the library elements may all be prepared from a single starting fluid or have a variety of starting fluids. In the case of cellular libraries prepared in mass from a collection of variants, such as genomically modified, yeast or bacteria cells, the library elements will be prepared from a variety of starting fluids.

Often it is desirable to have exactly one cell per droplet with only a few droplets containing more than one cell when starting with a plurality of cells or yeast or bacteria, engineered to produce variants on a protein. In some cases, variations from Poisson statistics may be achieved to provide an enhanced loading of droplets such that there are more droplets with exactly one cell per droplet and few exceptions of empty droplets or droplets containing more than one cell.

Examples of droplet libraries are collections of droplets that have different contents, ranging from beads, cells, small molecules, DNA, primers, antibodies. Smaller droplets may be in the order of femtoliter (fL) volume drops, which are especially contemplated with the droplet dispensers. The volume may range from about 5 to about 600 fL. The larger droplets range in size from roughly 0.5 micron to 500 micron in diameter, which corresponds to about 1 pico liter to 1 nano liter. However, droplets may be as small as 5 microns and as large as 500 microns. Preferably, the droplets are at less than 100 microns, about 1 micron to about 100 microns in diameter. The most preferred size is about 20 to 40 microns in diameter (10 to 100 picoliters). The preferred properties examined of droplet libraries include osmotic pressure balance, uniform size, and size ranges.

The droplets comprised within the emulsion libraries of the present invention may be contained within an immiscible oil which may comprise at least one fluorosurfactant. In some embodiments, the fluorosurfactant comprised within immiscible fluorocarbon oil is a block copolymer consisting of one or more perfluorinated polyether (PFPE) blocks and one or more polyethylene glycol (PEG) blocks. In other embodiments, the fluorosurfactant is a triblock copolymer consisting of a PEG center block covalently bound to two PFPE blocks by amide linking groups. The presence of the fluorosurfactant (similar to uniform size of the droplets in the library) is critical to maintain the stability and integrity of the droplets and is also essential for the subsequent use of the droplets within the library for the various biological and chemical assays described herein. Fluids (e.g., aqueous fluids, immiscible oils, etc.) and other surfactants that may be utilized in the droplet libraries of the present invention are described in greater detail herein.

The present invention provides an emulsion library which may comprise a plurality of aqueous droplets within an immiscible oil (e.g., fluorocarbon oil) which may comprise at least one fluorosurfactant, wherein each droplet is uniform in size and may comprise the same aqueous fluid and may comprise a different library element. The present invention also provides a method for forming the emulsion library which may comprise providing a single aqueous fluid which may comprise different library elements, encapsulating each library element into an aqueous droplet within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, wherein each droplet is uniform in size and may comprise the same aqueous fluid and may comprise a different library element, and pooling the aqueous droplets within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, thereby forming an emulsion library.

For example, in one type of emulsion library, all different types of elements (e.g., cells or beads), may be pooled in a single source contained in the same medium. After the initial pooling, the cells or beads are then encapsulated in droplets to generate a library of droplets wherein each droplet with a different type of bead or cell is a different library element. The dilution of the initial solution enables the encapsulation process. In some embodiments, the droplets formed will either contain a single cell or bead or will not contain anything, i.e., be empty. In other embodiments, the droplets formed will contain multiple copies of a library element. The cells or beads being encapsulated are generally variants on the same type of cell or bead. In one example, the cells may comprise cancer cells of a tissue biopsy, and each cell type is encapsulated to be screened for genomic data or against different drug therapies. Another example is that 10¹¹ or 10¹⁵ different type of bacteria; each having a different plasmid spliced therein, are encapsulated. One example is a bacterial library where each library element grows into a clonal population that secretes a variant on an enzyme.

In another example, the emulsion library may comprise a plurality of aqueous droplets within an immiscible fluorocarbon oil, wherein a single molecule may be encapsulated, such that there is a single molecule contained within a droplet for every 20-60 droplets produced (e.g., 20, 25, 30, 35, 40, 45, 50, 55, 60 droplets, or any integer in between). Single molecules may be encapsulated by diluting the solution containing the molecules to such a low concentration that the encapsulation of single molecules is enabled. In one specific example, a LacZ plasmid DNA was encapsulated at a concentration of 20 fM after two hours of incubation such that there was about one gene in 40 droplets, where 10 μm droplets were made at 10 kHz per second. Formation of these libraries rely on limiting dilutions.

The present invention also provides an emulsion library which may comprise at least a first aqueous droplet and at least a second aqueous droplet within a fluorocarbon oil which may comprise at least one fluorosurfactant, wherein the at least first and the at least second droplets are uniform in size and comprise a different aqueous fluid and a different library element. The present invention also provides a method for forming the emulsion library which may comprise providing at least a first aqueous fluid which may comprise at least a first library of elements, providing at least a second aqueous fluid which may comprise at least a second library of elements, encapsulating each element of said at least first library into at least a first aqueous droplet within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, encapsulating each element of said at least second library into at least a second aqueous droplet within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, wherein the at least first and the at least second droplets are uniform in size and comprise a different aqueous fluid and a different library element, and pooling the at least first aqueous droplet and the at least second aqueous droplet within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant thereby forming an emulsion library.

One of skill in the art will recognize that methods and systems of the invention are not limited to any particular type of sample, and methods and systems of the invention may be used with any type of organic, inorganic, or biological molecule (see, e.g, US Patent Publication No. 20120122714). In particular embodiments the sample may include nucleic acid target molecules. Nucleic acid molecules may be synthetic or derived from naturally occurring sources. In one embodiment, nucleic acid molecules may be isolated from a biological sample containing a variety of other components, such as proteins, lipids and non-template nucleic acids. Nucleic acid target molecules may be obtained from any cellular material, obtained from an animal, plant, bacterium, fungus, or any other cellular organism. In certain embodiments, the nucleic acid target molecules may be obtained from a single cell. Biological samples for use in the present invention may include viral particles or preparations. Nucleic acid target molecules may be obtained directly from an organism or from a biological sample obtained from an organism, e.g., from blood, urine, cerebrospinal fluid, seminal fluid, saliva, sputum, stool and tissue. Any tissue or body fluid specimen may be used as a source for nucleic acid for use in the invention. Nucleic acid target molecules may also be isolated from cultured cells, such as a primary cell culture or a cell line. The cells or tissues from which target nucleic acids are obtained may be infected with a virus or other intracellular pathogen. A sample may also be total RNA extracted from a biological specimen, a cDNA library, viral, or genomic DNA.

Generally, nucleic acid may be extracted from a biological sample by a variety of techniques such as those described by Maniatis, et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbor, N.Y., pp. 280-281 (1982). Nucleic acid molecules may be single-stranded, double-stranded, or double-stranded with single-stranded regions (for example, stem- and loop-structures).

Nucleic acid obtained from biological samples typically may be fragmented to produce suitable fragments for analysis. Target nucleic acids may be fragmented or sheared to desired length, using a variety of mechanical, chemical and/or enzymatic methods. DNA may be randomly sheared via sonication, e.g. Covaris method, brief exposure to a DNase, or using a mixture of one or more restriction enzymes, or a transposase or nicking enzyme. RNA may be fragmented by brief exposure to an RNase, heat plus magnesium, or by shearing. The RNA may be converted to cDNA. If fragmentation is employed, the RNA may be converted to cDNA before or after fragmentation. In one embodiment, nucleic acid from a biological sample is fragmented by sonication. In another embodiment, nucleic acid is fragmented by a hydroshear instrument. Generally, individual nucleic acid target molecules may be from about 40 bases to about 40 kb. Nucleic acid molecules may be single-stranded, double-stranded, or double-stranded with single-stranded regions (for example, stem- and loop-structures).

A biological sample as described herein may be homogenized or fractionated in the presence of a detergent or surfactant. The concentration of the detergent in the buffer may be about 0.05% to about 10.0%. The concentration of the detergent may be up to an amount where the detergent remains soluble in the solution. In one embodiment, the concentration of the detergent is between 0.1% to about 2%. The detergent, particularly a mild one that is nondenaturing, may act to solubilize the sample. Detergents may be ionic or nonionic. Examples of nonionic detergents include triton, such as the Triton™ X series (Triton™ X-100 t-Oct-C6H4-(OCH2-CH2)xOH, x=9-10, Triton™ X-100R, Triton™ X-114 x=7-8), octyl glucoside, polyoxyethylene(9)dodecyl ether, digitonin, IGEPAL™ CA630 octylphenyl polyethylene glycol, n-octyl-beta-D-glucopyranoside (betaOG), n-dodecyl-beta, Tween™. 20 polyethylene glycol sorbitan monolaurate, Tween™ 80 polyethylene glycol sorbitan monooleate, polidocanol, n-dodecyl beta-D-maltoside (DDM), NP-40 nonylphenyl polyethylene glycol, C12E8 (octaethylene glycol n-dodecyl monoether), hexaethyleneglycol mono-n-tetradecyl ether (C14E06), octyl-beta-thioglucopyranoside (octyl thioglucoside, OTG), Emulgen, and polyoxyethylene 10 lauryl ether (C12E10). Examples of ionic detergents (anionic or cationic) include deoxycholate, sodium dodecyl sulfate (SDS), N-lauroylsarcosine, and cetyltrimethylammoniumbromide (CTAB). A zwitterionic reagent may also be used in the purification schemes of the present invention, such as Chaps, zwitterion 3-14, and 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulf-onate. It is contemplated also that urea may be added with or without another detergent or surfactant.

Lysis or homogenization solutions may further contain other agents, such as reducing agents. Examples of such reducing agents include dithiothreitol (DTT), β-mercaptoethanol, DTE, GSH, cysteine, cysteamine, tricarboxyethyl phosphine (TCEP), or salts of sulfurous acid.

Size selection of the nucleic acids may be performed to remove very short fragments or very long fragments. The nucleic acid fragments may be partitioned into fractions which may comprise a desired number of fragments using any suitable method known in the art. Suitable methods to limit the fragment size in each fragment are known in the art. In various embodiments of the invention, the fragment size is limited to between about 10 and about 100 Kb or longer.

In another embodiment, the sample includes individual target proteins, protein complexes, proteins with translational modifications, and protein/nucleic acid complexes. Protein targets include peptides, and also include enzymes, hormones, structural components such as viral capsid proteins, and antibodies. Protein targets may be synthetic or derived from naturally-occurring sources. In one embodiment of the invention protein targets are isolated from biological samples containing a variety of other components including lipids, non-template nucleic acids, and nucleic acids. In certain embodiments, protein targets may be obtained from an animal, bacterium, fungus, cellular organism, and single cells. Protein targets may be obtained directly from an organism or from a biological sample obtained from the organism, including bodily fluids such as blood, urine, cerebrospinal fluid, seminal fluid, saliva, sputum, stool and tissue. Protein targets may also be obtained from cell and tissue lysates and biochemical fractions. An individual protein is an isolated polypeptide chain. A protein complex includes two or polypeptide chains. Samples may include proteins with post translational modifications including but not limited to phosphorylation, methionine oxidation, deamidation, glycosylation, ubiquitination, carbamylation, S-carboxymethylation, acetylation, and methylation. Protein/nucleic acid complexes include cross-linked or stable protein-nucleic acid complexes.

Extraction or isolation of individual proteins, protein complexes, proteins with translational modifications, and protein/nucleic acid complexes is performed using methods known in the art.

Methods of the invention involve forming sample droplets. The droplets are aqueous droplets that are surrounded by an immiscible carrier fluid. Methods of forming such droplets are shown for example in Link et al. (U.S. patent application numbers 2008/0014589, 2008/0003142, and 2010/0137163), Stone et al. (U.S. Pat. No. 7,708,949 and U.S. patent application number 2010/0172803), Anderson et al. (U.S. Pat. No. 7,041,481 and which reissued as RE41,780) and European publication number EP2047910 to Raindance Technologies Inc. The content of each of which is incorporated by reference herein in its entirety.

The present invention relates to systems and methods for manipulating droplets within a high throughput microfluidic system. Turning to FIG. 1 , a microfluid droplet (10) encapsulates a differentiated cell (not shown in the figure). The cell is lysed and its mRNA (20) is hybridized onto a capture bead containing barcoded oligo dT primers on the surface (30) (40), all inside the droplet. The barcode is covalently attached to the capture bead via a flexible multi-atom linker like PEG. (50). In a preferred embodiment, the droplets are broken by addition of a fluorosurfactant (like perfluorooctanol), washed, and collected. A reverse transcription (RT) reaction is then performed to convert each cell's mRNA into a first strand cDNA that is both uniquely barcoded and covalently linked to the mRNA capture bead. Subsequently, a universal primer via a template switching reaction is amended using conventional library preparation protocols to prepare an RNA-Seq library. Since all of the mRNA from any given cell is uniquely barcoded, a single library is sequenced and then computationally resolved to determine which mRNAs came from which cells. In this way, through a single sequencing run, tens of thousands (or more) of distinguishable transcriptomes can be simultaneously obtained.

Turning to FIGS. 2A and 2B, the oligonucleotide sequence generated on the bead surface is shown in FIG. 2A. During these cycles, beads were removed from the synthesis column, pooled, and aliquoted into four equal portions by mass; these bead aliquots were then placed in a separate synthesis column and reacted with either dG, dC, dT, or dA phosphoramidite. In other instances, dinucleotide, trinucleotides, or oligonucleotides that are greater in length are used, in other instances, the oligo-dT tail is replaced by gene specific oligonucleotides to prime specific targets (singular or plural), random sequences of any length for the capture of all or specific RNAs. This process was repeated 12 times for a total of 4¹²=16,777,216 unique barcode sequences (FIG. 2B). Upon completion of these cycles, 8 cycles of degenerate oligonucleotide synthesis were performed on all the beads, (the molecular barcode “MBC” in FIG. 2A) followed by 30 cycles of dT addition. In other embodiments, the degenerate synthesis is omitted, shortened (less than 8 cycles), or extended (more than 8 cycles); in others, the 30 cycles of dT addition are replaced with gene specific primers (single target or many targets) or a degenerate sequence.

In FIGS. 3A through 3D, one-thousand cell barcode sequences were analysed to determine cell barcode complexity (FIG. 3A).

The aforementioned microfluidic system is regarded as the reagent delivery system microfluidic library printer or droplet library printing system of the present invention (FIG. 4 ). Droplets (55) are formed as sample fluid flows from droplet generator (51) which contains lysis reagent and barcodes through microfluidic outlet channel (52) which contains oil (53), towards junction (54). Defined volumes of loaded reagent emulsion, corresponding to defined numbers of droplets, are dispensed on-demand into the flow stream of carrier fluid.

The sample fluid may typically comprise an aqueous buffer solution, such as ultrapure water (e.g., 18 mega-ohm resistivity, obtained, for example by column chromatography), 10 mM Tris HCl and 1 mM EDTA (TE) buffer, phosphate buffer saline (PBS) or acetate buffer. Any liquid or buffer that is physiologically compatible with nucleic acid molecules can be used. The carrier fluid may include one that is immiscible with the sample fluid. The carrier fluid can be a non-polar solvent, decane (e.g., tetradecane or hexadecane), fluorocarbon oil, silicone oil, an inert oil such as hydrocarbon, or another oil (for example, mineral oil).

In certain embodiments, the carrier fluid may contain one or more additives, such as agents which reduce surface tensions (surfactants). Surfactants can include Tween, Span, fluorosurfactants, and other agents that are soluble in oil relative to water. In some applications, performance is improved by adding a second surfactant to the sample fluid. Surfactants can aid in controlling or optimizing droplet size, flow and uniformity, for example by reducing the shear force needed to extrude or inject droplets into an intersecting channel. This can affect droplet volume and periodicity, or the rate or frequency at which droplets break off into an intersecting channel. Furthermore, the surfactant can serve to stabilize aqueous emulsions in fluorinated oils from coalescing.

In certain embodiments, the droplets may be surrounded by a surfactant which stabilizes the droplets by reducing the surface tension at the aqueous oil interface. Preferred surfactants that may be added to the carrier fluid include, but are not limited to, surfactants such as sorbitan-based carboxylic acid esters (e.g., the “Span” surfactants, Fluka Chemika), including sorbitan monolaurate (Span 20), sorbitan monopalmitate (Span 40), sorbitan monostearate (Span 60) and sorbitan monooleate (Span 80), and perfluorinated polyethers (e.g., DuPont Krytox 157 FSL, FSM, and/or FSH). Other non-limiting examples of non-ionic surfactants which may be used include polyoxyethylenated alkylphenols (for example, nonyl-, p-dodecyl-, and dinonylphenols), polyoxyethylenated straight chain alcohols, polyoxyethylenated polyoxypropylene glycols, polyoxyethylenated mercaptans, long chain carboxylic acid esters (for example, glyceryl and polyglyceryl esters of natural fatty acids, propylene glycol, sorbitol, polyoxyethylenated sorbitol esters, polyoxyethylene glycol esters, etc.) and alkanolamines (e.g., diethanolamine-fatty acid condensates and isopropanolamine-fatty acid condensates).

FIG. 5 illustrates a schematic of an apparatus for creating a single-cell sequencing library via a microfluidic system. In some cases, the device provides for volume-driven flow, wherein constant volumes are injected over time. The pressure in fluidic channels is a function of injection rate and channel dimensions. In an embodiment of the scheme according to FIG. 5 , the device provides a oil/surfactant inlet (60); an inlet for an analyte (70); a filter (80), an inlet for mRNA capture microbeads and lysis reagent (90); a carrier fluid channel which connects the inlets as illustrated in FIG. 5 ; a resistor (100); a constriction for droplet pinch-off (101); a mixer (110); and an outlet for drops (120). In an embodiment the invention provides apparatus for creating a single-cell sequencing library via a microfluidic system, comprising: a oil-surfactant inlet comprising a filter and a carrier fluid channel, wherein said carrier fluid channel further comprises a resistor; an inlet for an analyte comprising a filter and a carrier fluid channel, wherein said carrier fluid channel further comprises a resistor; an inlet for mRNA capture microbeads and lysis reagent comprising a filter and a carrier fluid channel, wherein said carrier fluid channel further comprises a resistor; said carrier fluid channels have a carrier fluid flowing therein at an adjustable or predetermined flow rate; wherein each said carrier fluid channels merge at a junction; and said junction being connected to a mixer, which contains an outlet for drops.

FIG. 6 illustrates a (a) Microfluidic flow scheme for single-cell RNA-seq. Two channels, one carrying cell suspensions, and the other carrying uniquely barcoded mRNA capture bead, lysis buffer and library preparation reagents meet at a junction and is immediately co-encapsulated in an inert carrier oil, at the rate of one cell and one bead per drop. In each drop, using the bead's barcode tagged oligonucleotides as cDNA template, each mRNA is tagged with a unique, cell-specific identifier. (b) Drop-Seq library of a mixture of mouse and human cells. Each dot represents a unique barcode, and indicates the number of genes that could aligned to human (x axis) and mouse (y axis) genomes.

FIG. 7 illustrates molecular barcoding of cellular transcriptomes in droplets. (A) Drop-Seq barcoding schematic. A complex tissue is dissociated into individual cells, which are then encapsulated in droplets together with microparticles (gray circles) that deliver barcoded primers. Each cell is lysed within a droplet; its mRNAs bind to the primers on its companion microparticle. The mRNAs are reverse-transcribed into cDNAs, generating a set of beads called “single-cell transcriptomes attached to microparticles” (STAMPs). The barcoded STAMPs can then be amplified in pools for high-throughput mRNA-seq to analyze any desired number of individual cells. (B) Sequence of primers on the microparticle. The primers on all beads contain a common sequence (“PCR handle”) to enable PCR amplification after STAMP formation. Each microparticle contains more than 10⁸ individual primers that share the same “cell barcode” (panel C) but have different unique molecular identifiers (UMIs), enabling mRNA transcripts to be digitally counted (panel D). A 30 bp oligo dT sequence (SEQ ID NO:1) is present at the end of all primer sequences for capture of mRNAs via their polyadenylated 3′ ends. (C) Split-and-pool synthesis of the cell barcode. To generate the cell barcode, the pool of microparticles is repeatedly split into four equally sized oligonucleotide synthesis reactions, to which one of the four DNA bases is added, and then pooled together after each cycle, in a total of 12 split-pool cycles. The barcode synthesized on any individual bead reflects that bead's unique path through the series of synthesis reactions. The result is a pool of microparticles, each possessing one of 4¹² (16,777,216) possible sequences on its entire complement of primers. (D) Synthesis of a unique molecular identifier (UMI). Following the completion of the “split-and-pool” synthesis cycles, all microparticles are together subjected to eight rounds of degenerate synthesis with all four DNA bases available during each cycle, such that each individual primer receives one of 4⁸ (65,536) possible sequences (UMIs).

FIG. 8 illustrates extraction and processing of single-cell transcriptomes by Drop-Seq. (A) Schematic of single-cell mRNA-Seq library preparation with Drop-Seq. A custom-designed microfluidic device joins two aqueous flows before their compartmentalization into discrete droplets. One flow contains cells, and the other flow contains barcoded primer beads suspended in a lysis buffer. Immediately following droplet formation, the cell is exposed to the lysis agent and releases its mRNAs, which then hybridize to the primers on the microparticle surface. The droplets are broken by adding a reagent to destabilize the oil-water interface (Extended Experimental Procedures), and the microparticles collected and washed. The mRNAs are then reverse-transcribed in bulk, forming STAMPs, and template switching is used to introduce a PCR handle downstream of the synthesized cDNA (Zhu et al., 2001). (B) Microfluidic device used in Drop-Seq. Beads (brown in image), suspended in a lysis agent, enter the device from the central channel; cells enter from the top and bottom. Laminar flow prevents mixing of the two aqueous inputs prior to droplet formation; this is evident in the image from the refraction of light along the interface of the two flows (see also Movie S1). (C) Molecular elements of a Drop-Seq sequencing library. The first read yields the cell barcode and UMI. The second, paired read interrogates sequence from the cDNA (50 bp is typically sequenced, though longer or shorter reads are also possible); this sequence is then aligned to the genome to determine a transcript's gene of origin. The cell barcode is used to determine the transcript's cell of origin. (D) In silico reconstruction of thousands of single-cell transcriptomes. Millions of paired-end reads are generated from a Drop-Seq library by a high-throughput sequencer (e.g. MiSeq, NextSeq, or HiSeq). The reads are first aligned to a reference genome to identify the gene-of-origin of the cDNA. Next, reads are organized by their cell barcodes, and individual UMIs are counted for each gene in each cell (Extended Experimental Procedures). The result, shown at far right, is a “digital expression matrix” in which each column corresponds to a cell, each row corresponds to a gene, and each entry is the integer number of transcripts detected from that gene, in that cell.

FIG. 9 illustrates critical evaluation of Drop-Seq using species-mixing experiments. (A,B) Drop-Seq analysis of mixtures of mouse and human cells. Mixtures of human (HEK) and mouse (3T3) cells were analyzed by Drop-Seq at the concentrations shown. The scatter plot shows the number of human and mouse transcripts associating to each STAMP. Blue dots indicate STAMPs that were designated from these data as containing human-specific sets of transcripts (average of 99% human transcripts); red dots indicate STAMPs inferred to be mouse-specific (average 99%). At the lower cell concentration, one STAMP barcode (of 570) associated with a mixture of human and mouse transcripts (panel A, purple). At the higher cell concentration, about 1.9% of STAMP barcodes associated with mouse-human mixtures (panel B). Data for other cell concentrations and a different single-cell analysis platform are in FIGS. 15C and 15D. (C,D) Sensitivity analysis of Drop-Seq at high read-depth. Violin plots show the distribution of the number of transcripts (B, scored by UMIs) and genes (C) detected per cell for 54 HEK (human) STAMPs (blue) and 28 3T3 (mouse) STAMPs (green) that were sequenced to a mean read depth of 737,240 high-quality aligned reads per cell. (E,F) Correlation between gene expression measurements in Drop-Seq and non-single-cell RNA-seq methods. Comparison of Drop-Seq gene expression measurements (averaged across 550 STAMPs) to measurements from bulk RNA analyzed in (E) an mRNA-seq library prepared by an in-solution template switch amplification (TSA) procedure similar to Smart-Seq2 (Picelli et al., 2013) (Extended Experimental Procedures); and (F) Illumina Tru-Seq mRNA-Seq. All comparisons involve RNA derived from the same cell culture flask (3T3 cells). All expression counts were converted to average transcripts per million (ATPM) and plotted as log (1+ATPM). (G) Quantitation of Drop-Seq capture efficiency by ERCC spike-ins. Drop-Seq was performed with ERCC control synthetic RNAs, spiked in at an estimated concentration of 100,000 ERCC RNA molecules per droplet. 84 STAMPs were sequenced at a mean depth of 2.4 million reads, aligned to the ERCC reference sequences, and UMIs counted for each ERCC species, after applying a stringent down-correction for potential sequencing errors (Extended Experimental Procedures). For each ERCC RNA species present at at least one molecule per droplet, the predicted number of molecules per droplet was plotted in log space (x-axis), versus the actual number of molecules detected per droplet by Drop-Seq, also in log space (y-axis). The intercept of a regression line, constrained to have a slope of 1 and fitted to the seven highest points, was used to estimate a conversion factor (0.128). A second estimation, using the average number of detected transcripts divided by the number of ERCC molecules used (100,000), yielded a conversion factor of 0.125.

FIG. 10 illustrates cell-cycle analysis of HEK and 3T3 cells analyzed by Drop-Seq. (A) Cell-cycle state of 589 HEK cells (left) and 412 3T3 cells (right) measured by Drop-Seq. Cells were assessed for their progression through the cell cycle by comparison of each cell's global pattern of gene expression with gene sets known to be enriched in one of five phases of the cycle (horizontal rows). A phase-specific score was calculated for each cell across each of these five phases (Extended Experimental Procedures), and the cells ordered by their phase scores. (B) Discovery of cell cycle regulated genes. Heat map showing the average normalized expression of 544 human and 668 mouse genes found to be regulated by the cell cycle in the Drop-Seq-sequenced cells. To find genes that were cell cycle regulated, maximal and minimal expression was calculated for each gene across a sliding window of the ordered cells, and compared with shuffled cells to obtain a false discovery rate (FDR) (Experimental Procedures). The plotted genes (FDR threshold of 5%) were then clustered by k-means analysis to identify sets of genes with similar expression patterns. Cluster boundaries are represented by dashed gray lines. (C) Representative cell cycle regulated genes discovered by Drop-Seq. Selected genes that were found to be cell cycle regulated in both the HEK and 3T3 cell sets. Left, selected genes that are well-known to be cell cycle regulated. On the right are some genes identified in this analysis that were not previously known to be associated with the cell cycle (Experimental Procedures). A complete list of cell cycle regulated genes can be found in Table 4.

FIG. 11 illustrates Ab initio reconstruction of retinal cell types from 44,808 single-cell transcription profiles prepared by Drop-Seq. (A) Schematic representation of major cell classes in the retina. Photoreceptors (rods or cones) detect light and pass information to bipolar cells, which in turn contact retinal ganglion cells that extend axons into other CNS tissues. Amacrine and horizontal cells are retinal interneurons; Müller glia act as support cells for surrounding neurons. (B) Clustering of 44,808 Drop-Seq single-cell expression profiles into 39 retinal cell populations. The plot shows a two-dimensional representation of global gene expression relationships among 44,808 cells; clusters are colored by cell class (colored according to FIG. 11A). (C) Differentially expressed genes across 39 retinal cell populations. In this heat map, rows correspond to individual genes found to be selectively upregulated in individual clusters (p<0.01, Bonferroni corrected); columns are individual cells, ordered by cluster (1-39). Clusters >1,000 cells were downsampled to 1,000 cells to prevent them from dominating the plot. (D) Gene expression similarity relationships among 39 inferred cell populations. Average gene expression across all detected genes was calculated for the cells in each of 39 cell clusters, and the relative (Euclidean) distances between gene-expression patterns for the 39 clusters were represented by a dendrogram. (The dendrogram represents global gene expression similarity relationships; it does not represent a developmental lineage.) The branches of the dendrogram were annotated by examining the differential expression of known markers for retina cell classes and types. Twelve examples are shown at right, using violin plots to represent the distribution of expression within the clusters. Violin plots for additional genes are in FIG. S6 . (E) Representation of experimental replicates in each cell population. tSNE plot from FIG. 8B, with each cell now colored by experimental replicate. Each of the 7 replicates contributes to all 39 cell populations. Cluster 36 (arrow), in which these replicates are unevenly represented, expressed markers of fibroblasts which are not native to the retina and are presumably a dissection artifact. (F) Trajectory of amacrine clustering as a function of number of cells analyzed. Three different downsampled datasets were generated: (1) 500, (2) 2,000, or (3) 9,451 cells (Extended Experimental Procedures). Cells identified as amacrines (clusters 3-23) in the full analysis are here colored by their cluster identities in that analysis. Analyses of smaller numbers of cells incompletely distinguished these subpopulations from one another.

FIG. 12 . Finer-scale expression distinctions among amacrine cells, cones and retinal ganglion cells. (A) Pan-amacrine markers. The expression levels of the six genes identified (Nrxn2, Atp1b1, Pax6, Slc32a1, Slc6a1, Elavl3) are represented as dot plots across all 39 clusters; larger dots indicate broader expression within the cluster; deeper red denotes a higher expression level. (B) Identification of known amacrine types among clusters. The twenty-one amacrine clusters consisted of twelve GABAergic, five glycinergic, one glutamatergic and three non-GABAergic non-glycinergic clusters. Starburst amacrines were identified in cluster 3 by their expression of Chat; excitatory amacrines were identified by expression of Slc17a8; A-II amacrines were identified in cluster 16 by their expression of Gjd2; and SEG amacrine neurons were identified in clusters 17 and 20 by their expression of Ebf3. (C) Nomination of novel candidate markers of amacrine subpopulations. Each cluster was screened for genes differentially expressed in that cluster relative to all other amacrine clusters (p<0.01, Bonferroni corrected) (McDavid et al., 2013), and filtered for those with highest relative enrichment. Expression of a single candidate marker for each cluster is shown across all retinal cell clusters (all genes differentially expressed in a cluster can be found in Table 6; genes differentially expressed between all cluster pairs can be found in Table 7). (D) Validation of MAF as a marker for a GABAergic amacrine population. Staining of a fixed adult retina from wild-type mice for MAF (panels i, ii, v, and green staining in iv and vii), GAD1 (panels iii and iv, red staining), and SLC6A9 (panels vi and vii, red staining; MAF staining is shown in green), demonstrating co-localization of MAF with GAD1, but not SLC6A9. (E) Differential expression of cluster 7 (MAF+) with nearest neighboring amacrine cluster (#6). Average gene expression was compared between cells in clusters 6 and 7; sixteen genes (red dots) were identified with >2.8- fold enrichment in cluster 7 (p<10⁻⁹). (F) Validation of PPP1R17 as a marker for an amacrine subpopulation. Staining of a fixed adult retina from Mito-P mice, which express CFP in both nGnG amacrines and type 1 bipolars (Kay et al., 2011). Asterisks (*) denote bipolar cells labeled in the Mito-P line, while arrows indicate the nGnG amacrine neurons, which are labeled by both the Mito-P transgenic line (red) and the PPP1R17 antibody (green). 85% of CFP+ cells were PPP1R17+; 50% of the PPP1R17+ were CFP−, suggesting a second amacrine type expressing this marker. (G) Differential expression of cluster 20 (PPP1R17+) with nearest neighboring amacrine cluster (#21). Average gene expression was compared between cells in clusters 20 and 21; twelve genes (red dots) were identified with >2.8-fold enrichment in cluster 7 (p<10⁻⁹). (H) Differential expression of M-opsin and S-opsin cones. Cells in cluster 25 were identified as cone photoreceptors, which express M-opsin (for detecting green light) and/or S-opsin (for detecting blue light). Average gene expression was compared between cells expressing M-opsin only (x-axis) and cells-expressing S-opsin only (y-axis). Eight genes showing greater than 2-fold differences in expression (p<10⁻⁹) are labeled on the plot along with the two opsin genes Opn1sw and Opn1mw. Green points are genes enriched in M-cones, while red points are genes enriched in S-cones. (I) Differential expression of melanopsin-positive and negative RGCs. Twenty-four retinal ganglion cells expressing Opn4, the gene encoding melanopsin, were identified in cluster 2 and average expression was compared between these cells and the remainder of cluster 2. Seven genes were identified as differentially expressed (red dots, >2-fold, p<10⁻⁹).

FIGS. 13A-C illustrate Ab initio reconstruction of human bone marrow cell types from 471 single-cell transcription profiles prepared by Drop-Seq. (A) Clustering of single-cell expression profiles into 8 cell classes. The plot shows a two-dimensional representation (tSNE) of global gene expression relationships among cells; clusters are colored and labeled by cell class. (B) A heatmap of differentially expressed genes across 8 cell classes. Rows correspond to individual marker genes; columns are individual cells, ordered by cluster (1-8). (C) Examples of marker genes expression (red is high) showed on tSNE map.

FIGS. 14A-C illustrate an assessment of the properties of barcoded primers on the surface of microparticles (beads). (A) Identification of individual bead barcodes in a multiplexed experiment. A synthetic polyadenylated RNA was reverse transcribed onto the surface of barcoded primer beads. Eleven of these beads were then manually selected and used as a template for construction of a sequencing library (Extended Experimental Procedures). The library was sequenced on a MiSeq, and the cell barcode sequences gathered and counted. A sharp distinction was observed between the numbers of reads carrying the eleventh and twelfth most abundant 12mers at the barcode position in the sequencing read, demonstrating that cell barcodes from each bead can be recognized from their high representation in the results of a sequencing experiment. (B) Base composition analysis of 12 bp cell barcodes. The sequences of 1,000 cell barcodes, ascertained in another sequencing experiment, were assessed for overall nucleotide and dinucleotide composition. Red dotted lines represent the values for completely random barcode sets that would lack any sequence bias. (C) Computational truncation of 12 bp cell barcodes. The 1,000 cell barcode sequences in (B) were trimmed from the 3′ end, and the number of unique barcodes remaining was calculated at each number of trimmed bases (blue line). The number of unique barcodes at each number of trimmings was compared to a randomly generated set of 1,000 12-mers (green line).

FIGS. 15A-E illustrate device design and dissection of technical contributions to single-cell impurities in Drop-Seq library preparations. (A) Microfluidic co-flow device design. Three inlets—for oil, cell suspension, and microparticles-converge and generate aqueous droplets composed of equal volume contributions from the cell suspension and microparticle channels. A winding, bumpy outlet improves mixing of the droplets to promote hybridization of released RNAs onto the beads. A CAD file of the device can be found in DataFile 1. (B) Identification of STAMPs in a pool of amplified beads. Drop-Seq involves generation of single-cell profiles by diluting cells to poisson-limiting concentrations in droplets; therefore, the great majority of amplified beads (90-99%) were not exposed to a cell's RNA, only ambient RNA. To identify the cell barcodes corresponding to STAMPs, cell barcodes from the experiment shown in FIG. 3A are arranged in decreasing order of size (number of reads), and the cumulative fraction of reads is plotted. An inflection point (vertical dotted line at 570) is observed very close to the number of cells predicted by Poisson statistics for the counted and aliquoted number of beads (˜500). Confirmation of this inflection point was observed by plotting the species specificity of individual STAMPs, and observing a dramatic drop in specificity at the inflection point, indicating the transition from beads that sampled cellular RNA, to the beads that sampled ambient RNA. (C) Human-mouse experiments on Fluidigm C1. Human (HEK) and mouse (3T3) cells were mixed at equal concentrations and run on two Fluidigm C1 chips according to the manufacturer's instructions. Reads were aligned to a joint human-mouse reference in exactly the same analysis pipeline as Drop-Seq. Fifty-six mixed-organism libraries were identified out of 182, placing a lower bound of 31% on cell-cell doublets. Twelve C1 ports were identified as possessing >1 cell by microscopy, of which five were mixed species by sequencing. (D) Concentration dependence of Drop-Seq library purity. STAMPs were prepared using a mixture of human (HEK) and mouse (3T3) cells at four different concentrations (N=1150, 690, 595, and 560 STAMPs for 100 cells/μl, 50 cells/μl, 25 cells/μl, and 12.5 cells/μl respectively). The rate of cell doublets was calculated by multiplying by two the number of mixed species STAMPs; single-cell purity was calculated by summing the mean human-cell and mean mouse-cell purities. (E) Single-cell impurity analysis. Drop-Seq libraries were prepared from combinations of human and mouse cells pooled at three different stages of DropSeq library preparation. In the first condition, human and mouse cells were mixed together prior to droplet formation (red violin plot, “Cell Mix”). In the second condition, human and mouse cells were separately encapsulated in droplets, which were then mixed before breaking them and performing subsequent analyses on the mixture (blue, “Droplet Mix”). In the third condition, human and mouse cells were separately encapsulated in droplets, which were broken in separate reactions and then reverse-transcribed to form separate pools of covalent STAMPs, which were mixed prior to PCR amplification (green, “PCR Mix”). The twenty largest STAMPs from each organism were selected for each of the three conditions, downsampled to the same read depth, and the organism purity represented as violin plots. The black dot is the average organism purity of the forty STAMPs in each distribution. The cell mixes used were diluted to a final concentration of 50 cells/μl in droplets. From these data Applicants estimate that (at this cell concentration) cell suspension contributes 48% of impurities, RNA transfer after droplet breakage contributes 40%, and PCR artifacts contribute 12%.

FIGS. 16A-F illustrate specificity and sensitivity as a function of sequencing coverage, evaluated by down-sampling low-depth and high-depth species-mixed (HEK/293T) Drop-Seq libraries prepared at a concentration of 50 cells/μl. (A,B) Analysis of specificity. Downsampling analysis of species specificity for human-specific STAMPs and mouse-specific STAMPs that were sequenced at lower read-depth (panel A, 589 human-specific and 412 mouse-specific STAMPs) or higher read-depth (panel B, 54 human and 28 mouse). (C-F) Analysis of sensitivity. Downsampling analysis of single-cell library sensitivity by average number of genes detected (C and D) and average number of transcripts detected (E and F) for the lower read-depth Drop-Seq run (C and E) and higher read-depth sequencing (D and F).

FIGS. 17A-F illustrate estimation of Drop-Seq expression bias and capture efficiency. (A) GC content bias between average gene expression in Drop-Seq and in-solution template-switch amplification (TSA). Comparison of average gene expression in low GC content genes (<0.4 average content, red dots) from a library of 550 3T3 STAMPs, and an mRNA-seq library prepared by an in-solution template switch amplification (TSA) procedure similar to Smart-Seq2 (Picelli et al., 2013) (Extended Experimental Procedures), using RNA derived from the same cell culture flask that was used in Drop-Seq. (B) GC content bias between average gene expression in Drop-Seq and standard mRNA-seq. Comparison of average gene expression in low GC content genes (<0.4 average content, red dots) from a library of 550 3T3 STAMPs, and an mRNA-seq library prepared by standard methods (Extended Experimental Procedures), using RNA derived from the same cell culture flask that was used in Drop-Seq. (C) Length bias between average gene expression in Drop-Seq and standard mRNA-seq. Comparison of average gene expression in long transcripts (>5000 average transcript length, red dots) from a library of 550 3T3 STAMPs, and an mRNA-seq library prepared by standard methods (Extended Experimental Procedures), using RNA derived from the same cell culture flask that was used in Drop-Seq. The bias observed here was not found in a comparison of Drop-Seq and in-solution TSA (data not shown), indicating that this bias is likely the result of template suppression PCR, which preferentially amplifies longer fragments (Zhu et al., 2001).

(D) Sensitivity estimation by ddPCR. RNA was isolated from a culture of 50,000 HEK cells, and levels of ten genes (ACTB, B2M, CCNB1, GAPDH, EEF2, ENO1, PSMB4, TOP2A, YBX3, and YWHAH) were digitally quantitated in this bulk solution using RT-ddPCR. These transcript counts were then compared to the average number of unique transcripts counted per cell by Drop-Seq. Error bars show the standard error for individual ddPCR measurements (horizontal bars, N=3 replicates) or across STAMPs (vertical bars, N=54). Based upon the mean of these ten gene expression measurements, Applicants estimate that DropSeq captures approximately 10.7% of cellular mRNAs. (E) Capture efficiency of barcoded primer beads. The same barcoded primer beads used in Drop-Seq were hybridized in solution to purified human brain RNA at a concentration of 20 ng/μl (Extended Experimental Procedures). The beads were then spun down and washed three times, and the bound RNA eluted by heating the beads in the presence of water. The concentrations of two mRNA transcripts, GAPDH and ACTB, were measured in each of the five steps. Error bars, standard error of the mean. (F) Assessment of barcoded bead primer binding saturation. The same procedure described in (E) was performed using three different input RNA concentrations: 20 ng/μl, 50 ng/μl and 100 ng/μl. The fraction of input RNA that was eluted off the beads scaled linearly with input RNA concentration, indicating that hybridization to the beads was not limited by a saturation of mRNA binding sites.

FIG. 18 illustrates plots of principal components 1-32 of the 44,808 retinal cell STAMPs used in analysis. (A) Uncolored PCA plots of 44,808 STAMPs; (B) the same PCA plots in (A), but each cell is colored by their final cluster identity, using the colors in FIG. 11B.

FIG. 19 illustrates violin plots showing expression of selected marker genes in the 39 retinal cell clusters generated by unsupervised analysis of single-cell gene expression.

FIG. 20 shows the fraction of each cluster composed of cells deriving from one of the seven replicates (prepared over four different days, (Extended Experimental Procedures), that composed the full 44,808-cell data set. The fractions of each replicate are represented as a stacked barplot. Replicates 1-6 were prepared in an “aggressive mode” of Drop-Seq (˜90% single-cell, ˜90% purity); replicate 7 was prepared in a “pure mode” (>99% single-cell, 98.6% purity). The stars designate two imbalanced cluster, #36, corresponding to contaminating fibroblasts that result from imperfect retinal dissection.

FIG. 21 illustrates a schematic representation of Drop-Seq setup. Three syringe pumps, loaded with oil, cells, and beads, respectively, are connected to the PDMS device in FIG. S2A via flexible tubing. The device rests on the stage of an inverted microscope so that droplet generation can be monitored in real-time. Tubing connects the outlet channel to a 50 mL conical tube for collection of droplets.

In certain embodiments, the carrier fluid may be caused to flow through the outlet channel so that the surfactant in the carrier fluid coats the channel walls. In one embodiment, the fluorosurfactant can be prepared by reacting the perfluorinated polyether DuPont Krytox 157 FSL, FSM, or FSH with aqueous ammonium hydroxide in a volatile fluorinated solvent. The solvent and residual water and ammonia can be removed with a rotary evaporator. The surfactant can then be dissolved (e.g., 2.5 wt %) in a fluorinated oil (e.g., Fluorinert (3M)), which then serves as the carrier fluid.

Activation of sample fluid reservoirs 1012 to produce regent droplets 1006 is now described. The disclosed invention is based on the concept of dynamic reagent delivery (e.g., combinatorial barcoding) via an on demand capability. The on demand feature may be provided by one of a variety of technical capabilities for releasing delivery droplets to a primary droplet, as described herein.

An aspect in developing this device will be to determine the flow rates, channel lengths, and channel geometries. Once these design specifications are established, droplets containing random or specified reagent combinations can be generated on demand and merged with the “reaction chamber” droplets containing the samples/cells/substrates of interest.

By incorporating a plurality of unique tags into the additional droplets and joining the tags to a solid support designed to be specific to the primary droplet, the conditions that the primary droplet is exposed to may be encoded and recorded. For example, nucleic acid tags can be sequentially ligated to create a sequence reflecting conditions and order of same. Alternatively, the tags can be added independently appended to solid support. Non-limiting examples of a dynamic labeling system that may be used to bioinformatically record information can be found at US Provisional Patent Application entitled “Compositions and Methods for Unique Labeling of Agents” filed Sep. 21, 2012 and Nov. 29, 2012. In this way, two or more droplets may be exposed to a variety of different conditions, where each time a droplet is exposed to a condition, a nucleic acid encoding the condition is added to the droplet each ligated together or to a unique solid support associated with the droplet such that, even if the droplets with different histories are later combined, the conditions of each of the droplets are remain available through the different nucleic acids. Non-limiting examples of methods to evaluate response to exposure to a plurality of conditions can be found at US Provisional Patent Application entitled “Systems and Methods for Droplet Tagging” filed Sep. 21, 2012.

Applications of the disclosed device may include use for the dynamic generation of molecular barcodes (e.g., DNA oligonucleotides, fluorophores, etc.) either independent from or in concert with the controlled delivery of various compounds of interest (drugs, small molecules, siRNA, CRISPR guide RNAs, reagents, etc.). For example, unique molecular barcodes can be created in one array of nozzles while individual compounds or combinations of compounds can be generated by another nozzle array. Barcodes/compounds of interest can then be merged with cell-containing droplets. An electronic record in the form of a computer log file is kept to associate the barcode delivered with the downstream reagent(s) delivered. This methodology makes it possible to efficiently screen a large population of cells for applications such as single-cell drug screening, controlled perturbation of regulatory pathways, etc. The device and techniques of the disclosed invention facilitate efforts to perform studies that require data resolution at the single cell (or single molecule) level and in a cost effective manner. Disclosed embodiments provide a high throughput and high resolution delivery of reagents to individual emulsion droplets that may contain cells, nucleic acids, proteins, etc. through the use of monodisperse aqueous droplets that are generated one by one in a microfluidic chip as a water-in-oil emulsion. Hence, the invention proves advantageous over prior art systems by being able to dynamically track individual cells and droplet treatments/combinations during life cycle experiments. Additional advantages of the disclosed invention provides an ability to create a library of emulsion droplets on demand with the further capability of manipulating the droplets through the disclosed process(es). Disclosed embodiments may, thereby, provide dynamic tracking of the droplets and create a history of droplet deployment and application in a single cell based environment.

Droplet generation and deployment is produced via a dynamic indexing strategy and in a controlled fashion in accordance with disclosed embodiments of the present invention. Disclosed embodiments of the microfluidic device described herein provides the capability of microdroplets that be processed, analyzed and sorted at a highly efficient rate of several thousand droplets per second, providing a powerful platform which allows rapid screening of millions of distinct compounds, biological probes, proteins or cells either in cellular models of biological mechanisms of disease, or in biochemical, or pharmacological assays.

A plurality of biological assays as well as biological synthesis are contemplated for the present invention.

In an advantageous embodiment, polymerase chain reactions (PCR) are contemplated (see, e.g., US Patent Publication No. 20120219947). Methods of the invention may be used for merging sample fluids for conducting any type of chemical reaction or any type of biological assay. In certain embodiments, methods of the invention are used for merging sample fluids for conducting an amplification reaction in a droplet. Amplification refers to production of additional copies of a nucleic acid sequence and is generally carried out using polymerase chain reaction or other technologies well known in the art (e.g., Dieffenbach and Dveksler, PCR Primer, a Laboratory Manual, Cold Spring Harbor Press, Plainview, N.Y. [1995]). The amplification reaction may be any amplification reaction known in the art that amplifies nucleic acid molecules, such as polymerase chain reaction, nested polymerase chain reaction, polymerase chain reaction-single strand conformation polymorphism, ligase chain reaction (Barany F. (1991) PNAS 88:189-193; Barany F. (1991) PCR Methods and Applications 1:5-16), ligase detection reaction (Barany F. (1991) PNAS 88:189-193), strand displacement amplification and restriction fragments length polymorphism, transcription based amplification system, nucleic acid sequence-based amplification, rolling circle amplification, and hyper-branched rolling circle amplification.

In certain embodiments, the amplification reaction is the polymerase chain reaction. Polymerase chain reaction (PCR) refers to methods by K. B. Mullis (U.S. Pat. Nos. 4,683,195 and 4,683,202, hereby incorporated by reference) for increasing concentration of a segment of a target sequence in a mixture of genomic DNA without cloning or purification. The process for amplifying the target sequence includes introducing an excess of oligonucleotide primers to a DNA mixture containing a desired target sequence, followed by a precise sequence of thermal cycling in the presence of a DNA polymerase. The primers are complementary to their respective strands of the double stranded target sequence.

To effect amplification, primers are annealed to their complementary sequence within the target molecule. Following annealing, the primers are extended with a polymerase so as to form a new pair of complementary strands. The steps of denaturation, primer annealing and polymerase extension may be repeated many times (i.e., denaturation, annealing and extension constitute one cycle; there may be numerous cycles) to obtain a high concentration of an amplified segment of a desired target sequence. The length of the amplified segment of the desired target sequence is determined by relative positions of the primers with respect to each other, and therefore, this length is a controllable parameter.

Methods for performing PCR in droplets are shown for example in Link et al. (U.S. Patent application numbers 2008/0014589, 2008/0003142, and 2010/0137163), Anderson et al. (U.S. Pat. No. 7,041,481 and which reissued as RE41,780) and European publication number EP2047910 to Raindance Technologies Inc. The content of each of which is incorporated by reference herein in its entirety.

The first sample fluid contains nucleic acid templates. Droplets of the first sample fluid are formed as described above. Those droplets will include the nucleic acid templates. In certain embodiments, the droplets will include only a single nucleic acid template, and thus digital PCR may be conducted. The second sample fluid contains reagents for the PCR reaction. Such reagents generally include Taq polymerase, deoxynucleotides of type A, C, G and T, magnesium chloride, and forward and reverse primers, all suspended within an aqueous buffer. The second fluid also includes detectably labeled probes for detection of the amplified target nucleic acid, the details of which are discussed below. This type of partitioning of the reagents between the two sample fluids is not the only possibility. In certain embodiments, the first sample fluid will include some or all of the reagents necessary for the PCR whereas the second sample fluid will contain the balance of the reagents necessary for the PCR together with the detection probes.

Primers may be prepared by a variety of methods including but not limited to cloning of appropriate sequences and direct chemical synthesis using methods well known in the art (Narang et al., Methods Enzymol., 68:90 (1979); Brown et al., Methods Enzymol., 68:109 (1979)). Primers may also be obtained from commercial sources such as Operon Technologies, Amersham Pharmacia Biotech, Sigma, and Life Technologies. The primers may have an identical melting temperature. The lengths of the primers may be extended or shortened at the 5′ end or the 3′ end to produce primers with desired melting temperatures. Also, the annealing position of each primer pair may be designed such that the sequence and, length of the primer pairs yield the desired melting temperature. The simplest equation for determining the melting temperature of primers smaller than 25 base pairs is the Wallace Rule (Td=2(A+T)+4(G+C)). Computer programs may also be used to design primers, including but not limited to Array Designer Software (Arrayit Inc.), Oligonucleotide Probe Sequence Design Software for Genetic Analysis (Olympus Optical Co.), NetPrimer, and DNAsis from Hitachi Software Engineering. The TM (melting or annealing temperature) of each primer is calculated using software programs such as Oligo Design, available from Invitrogen Corp.

A droplet containing the nucleic acid is then caused to merge with the PCR reagents in the second fluid according to methods of the invention described above, producing a droplet that includes Taq polymerase, deoxynucleotides of type A, C, G and T, magnesium chloride, forward and reverse primers, detectably labeled probes, and the target nucleic acid.

Once mixed droplets have been produced, the droplets are thermal cycled, resulting in amplification of the target nucleic acid in each droplet. In certain embodiments, the droplets are flowed through a channel in a serpentine path between heating and cooling lines to amplify the nucleic acid in the droplet. The width and depth of the channel may be adjusted to set the residence time at each temperature, which may be controlled to anywhere between less than a second and minutes.

In certain embodiments, the three temperature zones are used for the amplification reaction. The three temperature zones are controlled to result in denaturation of double stranded nucleic acid (high temperature zone), annealing of primers (low temperature zones), and amplification of single stranded nucleic acid to produce double stranded nucleic acids (intermediate temperature zones). The temperatures within these zones fall within ranges well known in the art for conducting PCR reactions. See for example, Sambrook et al. (Molecular Cloning, A Laboratory Manual, 3rd edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 2001).

In certain embodiments, the three temperature zones are controlled to have temperatures as follows: 95° C. (TH), 55° C. (TL), 72° C. (TM). The prepared sample droplets flow through the channel at a controlled rate. The sample droplets first pass the initial denaturation zone (TH) before thermal cycling. The initial preheat is an extended zone to ensure that nucleic acids within the sample droplet have denatured successfully before thermal cycling. The requirement for a preheat zone and the length of denaturation time required is dependent on the chemistry being used in the reaction. The samples pass into the high temperature zone, of approximately 95° C., where the sample is first separated into single stranded DNA in a process called denaturation. The sample then flows to the low temperature, of approximately 55° C., where the hybridization process takes place, during which the primers anneal to the complementary sequences of the sample. Finally, as the sample flows through the third medium temperature, of approximately 72° C., the polymerase process occurs when the primers are extended along the single strand of DNA with a thermostable enzyme.

The nucleic acids undergo the same thermal cycling and chemical reaction as the droplets pass through each thermal cycle as they flow through the channel. The total number of cycles in the device is easily altered by an extension of thermal zones. The sample undergoes the same thermal cycling and chemical reaction as it passes through N amplification cycles of the complete thermal device.

In other embodiments, the temperature zones are controlled to achieve two individual temperature zones for a PCR reaction. In certain embodiments, the two temperature zones are controlled to have temperatures as follows: 95° C. (TH) and 60° C. (TL). The sample droplet optionally flows through an initial preheat zone before entering thermal cycling. The preheat zone may be important for some chemistry for activation and also to ensure that double stranded nucleic acid in the droplets is fully denatured before the thermal cycling reaction begins. In an exemplary embodiment, the preheat dwell length results in approximately 10 minutes preheat of the droplets at the higher temperature.

The sample droplet continues into the high temperature zone, of approximately 95° C., where the sample is first separated into single stranded DNA in a process called denaturation. The sample then flows through the device to the low temperature zone, of approximately 60° C., where the hybridization process takes place, during which the primers anneal to the complementary sequences of the sample. Finally the polymerase process occurs when the primers are extended along the single strand of DNA with a thermostable enzyme. The sample undergoes the same thermal cycling and chemical reaction as it passes through each thermal cycle of the complete device. The total number of cycles in the device is easily altered by an extension of block length and tubing.

After amplification, droplets may be flowed to a detection module for detection of amplification products. The droplets may be individually analyzed and detected using any methods known in the art, such as detecting for the presence or amount of a reporter. Generally, the detection module is in communication with one or more detection apparatuses. The detection apparatuses may be optical or electrical detectors or combinations thereof. Examples of suitable detection apparatuses include optical waveguides, microscopes, diodes, light stimulating devices, (e.g., lasers), photo multiplier tubes, and processors (e.g., computers and software), and combinations thereof, which cooperate to detect a signal representative of a characteristic, marker, or reporter, and to determine and direct the measurement or the sorting action at a sorting module. Further description of detection modules and methods of detecting amplification products in droplets are shown in Link et al. (U.S. patent application numbers 2008/0014589, 2008/0003142, and 2010/0137163) and European publication number EP2047910 to Raindance Technologies Inc.

In another embodiment, examples of assays are ELISA assays (see, e.g., US Patent Publication No. 20100022414). The present invention provides another emulsion library which may comprise a plurality of aqueous droplets within an immiscible fluorocarbon oil which may comprise at least one fluorosurfactant, wherein each droplet is uniform in size and may comprise at least a first antibody, and a single element linked to at least a second antibody, wherein said first and second antibodies are different. In one example, each library element may comprise a different bead, wherein each bead is attached to a number of antibodies and the bead is encapsulated within a droplet that contains a different antibody in solution. These antibodies may then be allowed to form “ELISA sandwiches,” which may be washed and prepared for a ELISA assay. Further, these contents of the droplets may be altered to be specific for the antibody contained therein to maximize the results of the assay.

In another embodiment, single-cell assays are also contemplated as part of the present invention (see, e.g., Ryan et al., Biomicrofluidics 5, 021501 (2011) for an overview of applications of microfluidics to assay individual cells). A single-cell assay may be contemplated as an experiment that quantifies a function or property of an individual cell when the interactions of that cell with its environment may be controlled precisely or may be isolated from the function or property under examination. The research and development of single-cell assays is largely predicated on the notion that genetic variation causes disease and that small subpopulations of cells represent the origin of the disease. Methods of assaying compounds secreted from cells, subcellular components, cell-cell or cell-drug interactions as well as methods of patterning individual cells are also contemplated within the present invention.

In other embodiments, chemical prototyping and synthetic chemical reactions are also contemplated within the methods of the invention.

Although the present invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined in the appended claims.

The present invention will be further illustrated in the following Examples which are given for illustration purposes only and are not intended to limit the invention in any way.

EXAMPLES Example 1

In this protocol, uniquely barcoded beads are synthesized for use as primers for reverse transcription. Beads begin first with having a fixed sequence (SMT A in FIG. 2A) synthesized on the surface, which is used as a priming site for downstream PCR. Next, beads are split and pooled into four equal reaction vessels a total of 12 times, to generate 4{circumflex over ( )}12 unique barcode sequences that are unique to each bead (FIG. 2B). This 12 bp region will serve as the cell barcode, since it is specific to each bead. Next, the beads are all pooled together for 8 rounds of degenerate synthesis with all four bases; this 8 bp region is a “molecular barcode” and will tag each mRNA uniquely, so that each mRNA molecule in a cell can be digitally counted. Finally, 30 dT bases (SEQ ID NO:1) are synthesized, which serves as the capture region for the polyadenylated tails of mRNAs (referred to frequently in the literature as “oligo dT”).

Synthesis of Uniquely Barcoded Beads

Toyopearl HW-65S resin was purchased from Tosoh Biosciences, inc. Surface hydroxyls were reacted with a PEG derivative to generate an 18-carbon long, flexible-chain linker. The derivatized bead was then used as a solid support for reverse 5′->3′ phosphoramidite synthesis on an Expedite 8909 DNA/RNA synthesizer using DNA Synthesis 10 μmol cycle scale and a coupling time of 3 minutes. Amidites used were: N⁶-Benzoyl-3′-O-DMT-2′-deoxyadenosine-5′-cyanoethyl-N,N-diisopropyl-phosphoramidite (dA-N-Bz); N⁴-Acetyl-3′-O-DMT-2′-deoxy-cytidine-5′-cyanoethyl-N,N-diisopropyl-phosphoramidite (dC-N-Ac); N²-DMF-3′-O-DMT-2′-deoxyguanosine-5′-cyanoethyl-N,N-diisopropylphosphoramidite (dG-N-DMF); 3′-O-DMT-2′-deoxythymidine-5′-cyanoethyl-N,N-diisopropylphosphoramidite; and 3′-O-DMT-2′-deoxyuridine-5′-cyanoethyl-N,N-diisopropylphosphoramidite. Acetic anhydride and N-methylimidazole were used in the capping step; ethylthiotetrazole was used in the activation step; iodine was used in the oxidation step, and dichloroacetic acid was used in the deblocking step. The oligonucleotide sequence generated on the bead surface is shown in FIG. 2A. A constant sequence (“SMT A in figure) for use as a PCR handle, is synthesized. Then, 12 cycles of pool-and-split phosphoramidite synthesis are performed (the cell barcode or “CBC” in FIG. 2A). During these cycles, beads were removed from the synthesis column, pooled, and aliquoted into four equal portions by mass; these bead aliquots were then placed in a separate synthesis column and reacted with either dG, dC, dT, or dA phosphoramidite. This process was repeated 12 times for a total of 4{circumflex over ( )}12=16,777,216 unique barcode sequences (FIG. 2B). Upon completion of these cycles, 8 cycles of degenerate oligonucleotide synthesis were performed on all the beads, (the molecular barcode “MBC” in FIG. 2A) followed by 30 cycles of dT addition.

Characterization of Beads

1) Determination of bead binding capacity for polyadenylated RNA. Saturating quantities (100 μmol per 20,000 beads) of polyadenylated synthetic RNA was annealed to barcodes beads in 2×SSC for 5 min. The beads were then washed 3× with 200 ul of 1×TE+0.01% Tween, and resuspended in 10 ul of TE. The beads were then heated at 65 C for 5 min, and a ul of the supernatant was quantified on the Nanodrop Spectrophotometer at 260 nm.

2) Determination of quality and homogeneity of cell barcode sequences. Synthetic RNA was flowed into a 125 μl microfluidic co-flow droplet generation device at a concentration of 0.2 uM. The other flow contained a 2× reverse transcription mix. The droplets were incubated at 42° C. for 30 minutes, then broken. 11 beads were picked to a PCR tube and amplified with 17 cycles of PCR. The amplicon product was purified and quantified on the Bioanalyzer 2100, then sequenced on MiSeq. The cell barcode sequences were extracted and collapsed at edit distance 1 to obtain FIG. 3B.

3) Determination of cell barcode complexity. 1000 cell barcode sequences were analyzed for base composition (FIG. 3A), dinucleotide composition (FIG. 3B), and were serially trimmed from the 3′ end and checked for duplicate sequences (FIG. 3C). In all three analyses, the empirical cell barcodes displayed complexity that was only slightly below the theoretical limit of their complexity given their length (4{circumflex over ( )}12 unique sequences).

DropSeq Protocol

1. Reagents for preparing cells and beads for processing: Lysis Buffer (per mL): 680 μl H₂O 120 μl 50% Ficoll  10 μl 20% Sarkosyl  40 μl EDTA 100 μl 2M Tris pH 7.5  50 μl 1M DTT (add at the end)

PBS-BSA: 995 μl cold 1x PBS  5 μl NEB BSA (20 mg/ml)

Prepare the oil and device: Load oil into a 10 mL syringe. Affix needle (27G1/2) and tubing (PE-2), push oil through the tubing to the end, and load into pump. Place the tubing end in the left-most channel of a clean device (See FIG. 6 , all features on device are 125 μm deep).

Cell Culture

Human 293 T cells were purchased as well as murine NIH/3T3 cells. 293T and 3T3 cells were grown in DMEM supplemented with 10% FBS and 1% penicillin-streptomycin.

Cells were grown to a confluence of 30-60% and treated with TrypLE for five min, quenched with equal volume of growth medium, and spun down at 300×g for 5 min. The supernatant was removed, and cells were resuspended in 1 mL of 1×PBS+0.2% BSA and re-spun at 300×g for 3 min. The supernatant was again removed, and the cells re-suspended in 1 mL of 1×PBS, passed through a 40-micron cell strainer, and counted. For Drop-Seq, cells were diluted to the final concentration in 1×PBS+200 μg/mL BSA.

Generation of Whole Retina Suspensions

Single cell suspensions were prepared from P14 mouse retinas by adapting previously described methods for purifying retinal ganglion cells from rat retina (Barres et al., 1988). Briefly, mouse retinas were digested in a papain solution (40U papain/10 mL DPBS) for 45 minutes. Papain was then neutralized in a trypsin inhibitor solution (0.15% ovomucoid in DPBS) and the tissue was triturated to generate a single cell suspension. Following trituration, the cells were pelleted and resuspended and the cell suspension was filtered through a 20 m Nitex mesh filter to eliminate any clumped cells and this suspension was then used for Drop-Seq. The cells were then diluted in DPBS+0.2% BSA to either 200 cells/μl (replicates 1-6) or 30 cells/μl (replicate 7).

Retina suspensions were processed through Drop-Seq on four separate days. One library was prepared on day 1 (replicate 1); two libraries on day 2 (replicates 2 and 3); three libraries on day 3 (replicates 4-6); and one library on day 4 (replicate 7, high purity). To replicates 4-6, human HEK cells were spiked in at a concentration of 1 cell/μl (0.5%) but the wide range of cell sizes in the retina data made it impossible to calibrate single-cell purity or doublets using the cross-species comparison method. Each of the seven replicates was sequenced separately.

Preparation of Beads

Beads (either Barcoded Bead SeqA or Barcoded Bead SeqB) were washed twice with 30 mL of 100% EtOH and twice with 30 mL of TE/TW (10 mM Tris pH 8.0, 1 mM EDTA, 0.01% Tween). The bead pellet was resuspended in 10 mL TE/TW and passed through a 100 μm filter into a 50 mL Falcon tube for long-term storage at 4° C. The stock concentration of beads (in beads/μL) was assessed using a Fuchs-Rosenthal cell counter. For Drop-Seq, an aliquot of beads was removed from the stock tube, washed in 500 μL of Drop-Seq Lysis Buffer (DLB, 200 mM Tris pH 7.5, 6% Ficoll PM-400, 0.2% Sarkosyl, 20 mM EDTA), then resuspended in the appropriate volume of DLB+50 mM DTT for a bead concentration of 100 beads/μL.

Cell lysis and mRNA hybridization to beads on the microfluidic device. 1) Surfactant-containing oil; 2) cells suspended in aqueous solution (like PBS); and 3) barcoded beads suspended in a lysis agent (i.e., detergent). Cells and beads are flowed simultaneously into the device, where they unite and form droplets. Once inside the droplets, the cells lyse, RNA is released, and captured onto the surface of the barcoded bead by hybridization.

Syringe Pump: 14,000 μl/hr for oil; 4,100 μl/hr each for beads and cells; collect droplets in 50 mL falcon tubes; use 1 falcon tube per 1500 μl of aqueous solution (750 μl of each flow).

3. Post-Device Processing of RNA-Hybridized Beads into cDNA

BREAK DROPLETS: Immediately after completing droplet generation, remove oil from the bottom. Add 30 mL of room temperature 6x SSC. Shake. 6x SSC Add 600 μl of Perfluorooctanol (PFO). Mix well. Spin at 1000xg for 1 minute. Remove all but ~2-3 mL of liquid. Add 30 mL 6x SSC and spin again. Remove all but <1 mL of liquid. Transfer to eppendorf tubes and spin down to remove the supernatant. Wash 2x with 1 mL of 6x SSC then once with 300 μl of 5x RT buffer.

Reverse transcription: RT Mix (per 90,000 beads): 75 μl H₂O 40 μl Maxima 5x RT Buffer 40 μl 20% Ficoll PM-400 20 μl 10 mM dNTPs (Clontech)  5 μl RNase Inhibitor (Lucigen) 10 μl 50 μM Template Switch Oligo 10 μl Maxima H-RT (add just before starting RT)

Incubate and rotate at: RT for 30 minutes 42° C. for 90 minutes

Wash Wash beads once with TE + 0.5% SDS, then 2x with TE + TW (0.02%), then add 1 mL 10 mm Tris pH 7.5.

Microfluidic device is fabricated using polydimethylsiloxane (PDMS) from a master made of SU8 photo-resist1. The PDMS device is then plasma-treated to bond with a glass microscope slide (75 mm×50 mm×1 mm). Since we work with a continuous oil phase, the channels are rendered hydrophobic by flowing in Aquapel (Rider, Mass., USA) through the oil inlet and flushing out the excess fluid through the remaining inlets/outlets using pressurized air. See McDonald, J. C. et al. Fabrication of microfluidic systems in poly(dimethylsiloxane). Electrophoresis 21, 27 (2000).

Example 2: Genome-Wide Expression Profiling of Thousands of Individual Cells Using Nanoliter Droplets

Disease takes place within complex tissues, made of different types of cells, and (almost) never involves a single cell acting on its own: cells interact with each other constantly, making collective decisions, coordinating dynamic changes and working together. In normal tissue this results in homeostasis; in disease a malfunction in one or more interactions can lead to or exacerbate pathology.

Cells, the basic units of biological structure and function, vary broadly in type and state. Single cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here Applicants describe Drop-Seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, applying a different barcode to each cell's RNAs, and sequencing them all together. Drop-Seq analyzes mRNA transcripts from thousands of individual cells while remembering transcripts' cell of origin. Applicants analyzed transcriptomes from 44,808 mouse retinal cells and defined thirty-nine distinct cell populations, recapitulating the major retinal cell classes, identifying candidate markers of subtypes, and profiling gene expression in each. Applicants also analyzed 471 human bone marrow cells and defined eight distinct cell populations. Drop-Seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution.

Individual cells are the building blocks of tissues, organs, and organisms. Each tissue contains cells of many types, and cells of each type can switch among biological states. The number of cell types in a tissue can be over 100, and the number of states per cell is unknown. Because each type and state has unique functional capacities, responses and molecular compositions, it will be necessary to ascertain cell types and states to understand tissue physiology, developmental processes, and disease.

In most biological systems, Applicants' knowledge of cellular diversity is incomplete. For example, the cell-type complexity of the brain is unknown and widely debated (Luo et al., 2008; Petilla Interneuron Nomenclature et al., 2008). Many important but rare cell populations likely are undiscovered. Such rare types can play critical roles. Purkinje neurons, for example, are essential to brain function though they comprise less than 0.05% of neurons in the cerebellum (Andersen et al., 1992). Discovering a rare cell population may require analyzing large numbers of cells, ideally in an unbiased manner.

A major determinant of each cell's function is its transcriptional program. Recent advances now enable mRNA-seq analysis of individual cells (Kurimoto et al., 2006; Tang et al., 2009). HoFIGS.ver, current methods of preparing cells for profiling are applied to hundreds (Hashimshony et al., 2012; Islam et al., 2012; Picelli et al., 2013; Pollen et al., 2014; Shalek et al., 2014) or (with automation) a few thousand cells (Jaitin et al., 2014), typically after first separating the cells by sorting (Shalek et al., 2013), picking (Hashimshony et al., 2012), or microfluidics (Shalek et al., 2014), and then amplifying each cell's transcriptome in its own well or microfluidics chamber. Scalable approaches will be needed to characterize complex tissues with many cell types and states, under diverse conditions and perturbations. Profiling large numbers of cells may also be important for distinguishing noise from biologically meaningful patterns (sometimes involving small numbers of genes) that recur in many cells (Grun et al., 2014; Kharchenko et al., 2014).

The major obstacles to large-scale single-cell studies have been the cost and time involved in preparing large numbers of individual cells for sequencing. Here, Applicants describe a way to circumvent this obstacle by encapsulating thousands of individual cells in tiny “droplets”-nanoliter-scale aqueous compartments formed when water and oil mix-then barcoding the RNAs in each droplet in order to pool thousands of barcoded single-cell transcriptomes into one sample for sequencing. While single mRNA-sequence analysis is presently described, other types of nucleotides can be captured such as DNA and viruses from a cell or any molecular compound which can leverage phosphoramidite chemistry. Microfluidic devices can create tens of thousands of precisely sized (“monodisperse”) picoliter- or nanoliter-scale droplets per minute (Thorsen et al., 2001; Umbanhowar, 2000). These droplets, which serve as tiny reaction chambers, have been used for PCR (Hindson et al., 2011; Vogelstein and Kinzler, 1999), reverse transcription (Beer et al., 2008), cell viability screens (Brouzes et al., 2009), and fluorescence microscopy (Jarosz et al., 2014). However, a basic challenge of using droplets for transcriptomics is to retain a molecular memory of the identity of the cell from which each mRNA transcript was isolated. The lack of effective molecular barcoding has prevented the application of droplets in many areas of genetics and genomics (Guo et al., 2012).

Here, Applicants address this challenge by introducing a barcoding system that endows each transcript with a droplet-specific molecular tag. Applicants' method, called Drop-Seq, combines droplet microfluidics with massive molecular barcoding to simultaneously label and process the mRNA transcripts from thousands of cells in one reaction for sequencing, without requiring mechanical sorting or picking of individual cells.

To demonstrate Drop-Seq's power to categorize cells in complex tissues, Applicants applied it to mouse retina. The retina is a powerful model for analysis of neural structure, function and development because, although it is about as complicated as any other part of the brain, it provides a complete and accessible circuit in a compact volume (Hoon et al., 2014; Masland, 2012; Masland and Sanes, 2015; Sanes and Zipursky, 2010). The retina contains five neuronal classes that are divided into ˜100 types, only a minority of which have been molecularly characterized. Applicants used Drop-Seq to analyze 44,808 single cells from the mouse retina, from which Applicants computationally assembled an ab initio cell classification of 39 cell types based solely on patterns among the transcriptional profiles of many individual cells. This classification reproduces—in a single experiment—discoveries from decades of molecular, physiological, and anatomical investigations of the retina, while nominating many novel putative subtypes and specific markers. The results suggest how large-scale single-cell analysis will deepen Applicants' understanding of the biology of complex tissues and cell populations.

To further demonstrate Drop-Seq's capability and capacity to categorize cells in complex tissues, Applicants applied Drop-Seq in human bone marrow cells. Applicants explored human bone marrow cellular complexity on a limited number of cells and confirmed known key classifications based solely on their profiles.

Results

To efficiently profile vast numbers of individual cells, Applicants developed Drop-Seq, in which Applicants encapsulate cells in tiny droplets and barcode the transcripts from each individual droplet (encapsulated cell) to remember their cell of origin. Drop-Seq consists of the following steps (FIG. 7A): (1) prepare a single-cell suspension from a tissue; (2) co-encapsulate each individual cell with one distinctly barcoded microparticle, bead or particle (e.g., microbead, macrobead, nanoparticle, etc.) in a nanoliter-scale droplet; (3) lyse cells only after they have been isolated in droplets; (4) capture a cell's mRNAs on its companion microparticle, forming STAMPs (Single-cell Transcriptomes Attached to Microparticles); (5) reverse-transcribe, amplify, and sequence thousands of STAMPs in a single reaction; and (6) use the STAMP barcodes to infer each transcript's cell of origin. Applicants describe the key components of this approach and their validation.

A split-pool synthesis approach to generating large numbers of distinctly barcoded beads. The split-and-pool can occur after each cycle, or after any specified number of cycles. Thus, each barcode of information can range from a single nucleotide, to a dinucleotide or trinucleotide, etc.

To deliver large numbers of barcoded primer molecules into individual droplets, Applicants synthesized oligonucleotides directly on beads. As a bead material, Applicants used a methacrylate resin, originally developed for chromatography (Extended Experimental Procedures), composed of porous microparticles with substantial surface area. A variety of bead materials are envisioned as useful bead substrates. Examples of bead materials which may be employed include any bead which can leverage phosphoramidate chemistry such as those used in oligonucleotide synthesis known to those skilled in the art. Specific examples include, but are not limited to, functionalized polymers (e.g., methylacrylates, polysterenes, polyacrylamides, polyethyleneglycols), paramagnetic beads, and magnetic beads.

Applicants then used reverse-direction phosphoramidite synthesis to build oligonucleotides outwards from the microparticles from 5′ to 3′, yielding free 3′ ends available for enzymatic priming (Cheong et al., 2012; Kadonaga, 1991; Srivastava et al., 2008). Phosphoramidite synthesis which is used to generate the barcodes, enables the chemical modification of any base along the oligonucleotide which can leverage this type of chemistry. Specific examples include, but are not limited to, barcoding with DNA bases, RNA bases, LNA bases, biotin-modified bases, fluorophore-conjugated bases, and non-canonical bases (i.e., iso-G, iso-C, iso-A, etc.). Additionally, these barcoded beads can be combined with other forms of barcoding, such as optional barcoding by patterning the bead or fluorescent labelling with various fluorophores or combinations of fluorophores.

Each microparticle-bound oligonucleotide is composed of five parts (FIG. 7B): (1) a constant sequence (identical on all primers) for use as a priming site for PCR and sequencing; (2) a “cell barcode” that is the same across all the primers on the surface of any one bead, but different from the cell barcodes on all other beads; (3) a Unique Molecular Identifier (UMI), different on each primer, that enables sequence reads derived from the same original mRNA molecule (amplification and PCR duplicates) to be identified computationally so that they are not double-counted (Kivioja et al., 2012); (4) an oligo dT sequence (30 bases) (SEQ ID NO:1) for capturing polyadenylated mRNAs and priming reverse transcription, and (5) a non-cleavable linker attached to the surface of the bead material (not labelled) and the priming sequence.

To efficiently generate massive numbers of beads, each with millions of copies of a cell barcode distinct from the barcodes on the other beads, Applicants developed a “split-and-pool” synthesis strategy (FIG. 7C). A pool of millions of microparticles is divided into four equally sized groups; a different DNA base (A, G, C, or T) is added to each of the four groups. The four groups of microparticles are then re-pooled, mixed, and re-split at random into another four groups, and another DNA base (A, G, C, or T) is added to each of the four new groups. After repeating this split-pool process 12 times, each bead's barcode reflects that bead's unique path through twelve synthesis reactions (FIG. 7C), such that all primers on a single microparticle possess the same one of 4¹²=16,777,216 possible 12-bp barcodes. The entire microparticle pool then undergoes eight rounds of degenerate oligonucleotide synthesis to generate the UMI on each oligo (FIG. 7D); finally, an oligo dT sequence (T30) (SEQ ID NO:1) is synthesized on 3′ the end of all oligos on all beads.

In various embodiments of oligonucleotide bound bead synthesis, optional “floppy bases” may be used, such as oligo dT which is presently described. However, these “floppy bases” are not limited to T-bases and any suitable base can be used anywhere from 0 to 20 bases.

While microbeads are presently described, this method is not limited to “micro” sized beads and any appropriately sized bead is useful in an application where primers, PCR templates, transposons, siRNAs, or capture probes are delivered to a target compartment. The bead can simultaneously deliver both oligonucleotides and other chemical compounds, biological particles, or even reagents. Examples include but are not limited to a small molecule library, siRNA, an antibody, a virus, a bacterium, and so on. Thus, the bead size is related to the application of the bead. For example, a bead which is 1 cm in diameter can accommodate millions of primers then deliver the primers to a 96-well titer plate, where then the linker is cleaved to release and deliver the primers to these wells. Cleavable linkers can include a variety of polymers (or other types of “flexible” strain-chain compound) which hydrolyze under aqueous acidic or basic conditions, undergo photolysis, cleave under hydrogenation, or any method known to one of skill in the art to release the bead from the mRNA or nucleotide sequence.

Applicants assessed the quality and complexity of Applicants' barcoded beads in several ways. First, to estimate the number of primers per microparticle, Applicants hybridized synthetic polyadenylated RNA to microparticles, eluted the synthetic RNA, and measured its concentration; from these experiments, Applicants estimate that each bead contains more than 108 primer sites (Extended Experimental Procedures). Second, to determine the ability to distinguish RNA based on attached barcodes, Applicants reverse-transcribed synthetic RNA hybridized to 11 microparticles, amplified these barcoded cDNAs in a single solution, and created a sequencing library (Extended Experimental Procedures). In the resulting sequence data, 11 cell barcodes each constituted 3.5%-14% of the sequencing reads, whereas the next most abundant 12-mer at the barcode position constituted only 0.06% of reads (FIG. 14A). These results suggested that the microparticle-of-origin for most cDNAs can be recognized by sequencing. Finally, to assess the barcode complexity, Applicants sequenced cell barcodes from 1,000 microparticles and measured base and dinucleotide composition (FIG. 14B), along with the number of unique cell barcodes that remained as the sequence was computationally truncated (FIG. 14C). All three analyses suggested that the sequence diversity of the cell barcodes approached theoretical limits, and therefore that the cell barcodes could easily discriminate among thousands of STAMPs.

Microfluidics device for co-encapsulating cells with beads. Applicants designed a microfluidic “co-flow” device (Utada et al., 2007) to co-encapsulate cells with barcoded microparticles (FIGS. 8A, 14A). This device can quickly co-flow two aqueous solutions across an oil channel to form more than 50,000 nanoliter-sized droplets per minute. One flow contains the barcoded microparticles, suspended in a lysis buffer; the other flow contains a cell suspension (FIG. 8A, left). Flow is laminar prior to encapsulation, so that the two solutions mix only after droplet formation. To maximize cell lysis and the diffusion of mRNAs onto the bead's surface, Applicants' device contains “mixers” in which rapid mixing by chaotic advection occurs in a bumpy, winding microfluidic channel (Bringer et al., 2004).

The relative numbers of droplets, cells, and microparticles are key to the efficacy of Drop-Seq. The number of droplets created greatly exceeds the number of beads or cells injected, so that a droplet will generally contain zero or one cells, and zero or one beads. Carefully selecting the concentration of cells is also important for regulating cell-cell doublets and potential single-cell impurities, as Applicants discuss below. Millions of nanoliter-sized droplets are generated per hour, of which thousands contain both a bead and a cell. STAMPs are produced only in the subset of droplets that contain both a bead and a cell.

Sequencing and analysis of many STAMPs in a single reaction. To efficiently analyze thousands of STAMPs at once, Applicants developed a way to process the nucleic acids bound to any desired number of microparticles in one reaction. Applicants first break the droplets in a large volume of high-salt solution, to minimize the transfer of RNAs from bead to bead (Experimental Procedures). The mRNAs associated with the microparticles are then reverse-transcribed together in one reaction, forming covalent STAMPs (FIG. 8A, step 7). (Reverse transcription can in principle be performed within the droplets, though Applicants found it to be more efficient outside the droplets, potentially due to cell lysate-derived factors that inhibit the reaction (White et al., 2011).) Critically, at this stage, a scientist can select any desired number of STAMPs for analysis, much as one would select a desired number of cells from a cell suspension. STAMPs can be “banked” across multiple experiments; Applicants have stored STAMPs for more than two months without observing significant cDNA degradation (data not shown). Applicants PCR-amplify the barcoded cDNAs attached to STAMPs, then prepare 3′-end libraries by using a transposase to insert a sequencing adapter into the cDNA (Experimental Procedures). Applicants sequence the resulting molecules from each end (FIG. 8C) using high-capacity parallel sequencing (e.g., Illumina MiSeq, NextSeq, or HiSeq), and use these reads to assemble a matrix of digital gene-expression measurements (counts of each gene in each cell) for further analysis (FIG. 8D, Experimental Procedures).

Drop-Seq has high single-cell specificity, as assessed in species-mixing experiments. To determine whether Drop-Seq correctly remembers the cell from which individual transcripts were isolated, Applicants designed species-mixing experiments in which Applicants made suspensions containing cultured human (HEK) and mouse (3T3) cells. Nearly all human or mouse mRNA sequence fragments can be unambiguously assigned to the correct genome of origin; a cell library's “organism purity” can therefore be used to estimate its single-cell purity.

Applicants prepared Drop-Seq libraries from mixtures of human and mouse cells, scoring the numbers of human and mouse transcripts that associated with each cell barcode in the sequencing data (FIGS. 9A, 9B, 14B). This analysis revealed that STAMPs associated to highly organism-specific sets of transcripts (FIGS. 9A and 9B), a result that would not be possible without high single-cell specificity. At deep levels of sequencing that largely saturated sequencing of 82 STAMPs (737,240 reads per cell, FIG. 15 ) Applicants detected an average of 44,295 transcripts from 6,722 genes in HEK cells, and 26,044 transcripts from 5,663 genes in 3T3 cells (FIGS. 9C and 9D).

Single-cell purity of Drop-Seq libraries. It is important to understand the limitations as well as the strengths of new technologies. Applicants therefore characterized two sources of impurity in single-cell libraries.

Cell doublets. One mode of failure in any single-cell method involves cells that stick together or happen to otherwise be co-isolated for library preparation. In some earlier methods, microscopy imaging of wells has been used to identify “visible doublets” and establish a lower bound on doublet rates. A previous study that used FACS to sort single cells reported that 2.3% of wells contained visible cell doublets (Jaitin et al., 2014). The main commercial single-cell analysis platform (Fluidigm C1) images sets of 96 microfluidically isolated cells, in part so that users can identify doublets from these images; one recent study identified visible doublets in 11%±9% of the capture chambers that contained cells (Shalek et al., 2014).

Molecular analysis by species mixing offers a powerful and sensitive new way to identify libraries prepared from doublets, and may identify many doublets that are not detected by microscopy. For example, when Applicants prepared species-mixed cell populations exactly as in the analysis of Drop-Seq (FIGS. 9A, 9B) and analyzed them on the Fluidigm C1, Applicants found 30% of the prepared libraries to be species-mixed (FIG. 14C) of which about one-third were visible doublets in the microscopy images. When Applicants prepared Drop-Seq libraries from cell suspensions at a cell concentration of 12.5 cells/μl (that allows processing of about 1,200 cells per hour), almost all libraries were species-specific (FIG. 9A). When Applicants prepared Drop-Seq libraries from cell suspensions at a higher cell concentration (50 cells/μl), accommodating faster processing of cells (4,800 cells/hour), 1.9% of the sequenced STAMPs were species-mixed (FIG. 9B). Across four conditions spanning 12.5 cells/μl to 100 cells/μl, there was a strong linear relationship between the cell concentration used and the fraction of species-mixed STAMPs (FIG. 15D; Experimental Procedures), reflecting the greater chance that droplets encapsulate both a mouse and a human cell at higher cell concentrations. Since human-mouse doublets account for half of all cell-cell doublets, Applicants calculated overall doublet rates of 0.36% to 11.3% for the Drop-Seq conditions ranging from highest-purity to highest-throughput.

Single-cell impurity. A largely unexplored issue in single-cell analysis involves the extent to which single-cell libraries become contaminated with transcripts from other cells. The high throughput of Drop-Seq and Applicants' use of species-mixing experiments allowed us to carefully measure single-cell purity across thousands of single-cell libraries prepared at different cell concentrations. Applicants found that impurity was strongly related to the concentration at which cell suspensions were loaded: organism purity ranged from 98.8% at 12.5 cells/μl to 90.4% at 100 cells/μl (FIG. 15D). By mixing human and mouse cell-to-library pipelines at different stages (cell suspension; droplets containing beads and lysed cells; post-droplet STAMPs), Applicants found that the cell suspension contributed 48% of impurities, RNA transfer after droplet breakage contributed 40%, and PCR artifacts contributed 12% (FIG. 15E). Thus, the largest source of contamination appears to be ambient RNA that is present in the cell suspension at the beginning of the experiment and presumably results from cells that are damaged during preparation. This result is important for single-cell transcriptomics studies, as the creation of cell suspensions is an indispensable first step of almost all such methods. Indeed, when Applicants analyzed the same species-mixed cell populations on a commercial single-cell sequencing platform (Fluidigm C1), Applicants measured a mean single-cell purity of 95.8% (FIG. 15C), similar to Drop-Seq at 50 cells/μl. It will be important to carefully evaluate all single-cell methods using the kinds of species-mixing experiments performed here.

While the high-purity modes of Drop-Seq (FIG. 9A) would seem preferable to the highest-throughput modes (FIG. 9B) on these grounds, Applicants note that in may experimental contexts it may be desirable to process living cells as quickly as possible, because ultra-fast processing of living cells may strengthen reproducibility and thereby help to realize a potential strength of Drop-Seq relative to slower-throughput, existing methods. Applicants further explore these questions in the retina experiments below.

Drop-Seq samples about 12% of the transcripts in a cell. Applicants next sought to understand how the digital single-cell transcriptomes ascertained by Drop-Seq relate to the underlying mRNA content of cells.

Drop-Seq involves hybridization of RNAs to beads, which might affect measurements of genes' absolute expression levels, so Applicants compared Drop-Seq expression measurements to those from a commonly used in-solution cDNA amplification process, template switch amplification (Extended Experimental Procedures). While template switch amplification is presently described, T7 linear amplification or exponential isothermal amplification can also be used to amplify the product. Gene-level log-expression measurements in the two libraries were highly correlated (r=0.94, FIG. 9E), though Drop-Seq showed quantitatively lower ascertainment of GC-rich transcripts (FIG. 17A). Applicants also compared Drop-Seq single-cell log-expression measurements with measurements from bulk mRNA-seq, and observed a correlation of r=0.90 (FIG. 9F).

An important and longstanding challenge in single-cell transcriptomics is to understand how the RNAs ascertained in an experiment relate to the original RNA contents of the cells. The increasing use of External RNA Controls Consortium (ERCC) “spike-in” controls at known concentrations, together with UMIs to avoid double-counting, now allows estimation of capture rates for digital single-cell expression technologies (Brennecke et al., 2013). Three recent studies estimated capture rates of current single-cell digital-expression technologies at 3% (MARS-Seq) (Jaitin et al., 2014), 3.4% (CEL-Seq) (Grun et al., 2014), and 48% (5′-end SMART-seq) (Islam et al., 2014). Estimation of Drop-Seq capture rates using the correction method of Islam et al. (to try to avoid double-counting UMIs due to PCR or sequencing errors), generated a capture-rate estimate of 47% for Drop-Seq; however, Applicants identified evidence that sequencing errors can still inflate UMI counts, even when that correction method is used (Extended Experimental Procedures), so Applicants utilized the 8 bp UMI in Drop-Seq to derive a more conservative estimate (12.8%, FIG. 9G) based on a novel approach of collapsing similar UMI sequences into a single count. To further evaluate capture rates, Applicants made independent digital expression measurements (on bulk RNA from 50,000 HEK cells) on 10 genes using droplet digital PCR (ddPCR) (Hindson et al., 2011). Drop-Seq captured on average 10.7% of the number of RNAs predicted by digital PCR (FIGS. 17D, 17E, and 17F). These data indicate that the sensitivity of Drop-Seq is within the range established by recently developed digital expression methods, even when Applicants' novel and extremely conservative UMI counting method is used to evaluate Drop-Seq.

Single-cell analysis of the cell cycle reveals continuously varying cell states. To evaluate the visibility of cell states by Drop-Seq, Applicants first examined cell-to-cell variation among the 589 HEK and 412 3T3 cells for which Applicants had prepared STAMPs in the above experiment (61,697 reads per cell). Both cultures consist of asynchronously dividing cells; principal components analysis (PCA) of the single-cell expression profiles showed the top components to be dominated by genes with roles in protein synthesis, growth, DNA replication, and other aspects of the cell cycle (Table 5). Applicants inferred the cell-cycle phase of each of the 1,001 cells by scoring for gene sets (signatures) reflecting five phases of the cell cycle previously characterized in chemically synchronized cells (G1/S, S, G2/M, M, and M/G1) (Table 6) (Whitfield et al., 2002). Genes in each signature co-varied across individual cells, allowing us to temporally order the cells along the cell cycle (FIG. 10A). Using this ordering, Applicants identified genes with expression patterns that vary along the cell cycle (at a false discovery rate of 5%; Experimental Procedures), yielding 544 and 668 genes in human (HEK) and mouse (3T3) cells, respectively (FIG. 10B). Most of the genes had peak expression in either the G1+S or in the G2+M phases (FIG. 10B), with a minority displaying other patterns, such as peak expression at the M/G1 transition (e.g. cluster 8 in mouse cells, FIG. 10B). Among these genes, there was a significant overlap in orthologous genes between the two species (200 shared orthologs, P<10⁻⁶⁵ by hypergeometric test), consistent with a conserved cell cycle program. Most (82.5%) of these “conserved” cycling genes (the genes identified as cell cycle regulated in both species) have been previously annotated as related to the cell cycle in at least one species. Among the 17.5% of conserved cycling genes that were not previously annotated as cell-cycle-regulated, Applicants found some that would be expected to show cell cycle variation (e.g. E2F7, NCAPG, CDCA4, DNMT1 and PARPBP), as well as some that to Applicants' knowledge were not previously connected to the cell cycle, including transcription factors (TCF19, ATF4, ZFHX4) and other genes (FIG. 10C).

Finally, Applicants found that in each species, four of the five top PCs were highly correlated with at least one of the cell cycle phase-specific scores (P<10⁻¹⁰), indicating a dominant role of the cell cycle in cell-to-cell variation in these cells, consistent with other reports in dividing cells (Buettner et al., 2015). Thus, Drop-Seq single-cell profiles can uncover sets of genes that vary according to subpopulation phenotypes. In particular, this enables study of the cell cycle without chemical synchronization and at high temporal resolution across a large number of cells, which may have assisted in identifying conserved human-mouse gene pairs not previously known to oscillate with the cell cycle.

Drop-Seq analysis of the retina reveals cell classes. Applicants selected the retina to study with Drop-Seq because work over many decades has generated information about many retinal cell types (Masland, 2012; Sanes and Zipursky, 2010), providing an opportunity to relate Applicants' single-cell RNA-seq data to existing cell classification schemes. The retina contains five classes of neuronal cells, each defined by a combination of morphologic, physiologic, and molecular criteria (FIG. 11A). The outermost of three cellular layers contains photoreceptors, which transduce light into electrical signals. The middle layer contains three classes of interneurons—horizontal, bipolar and amacrine cells—as well as Müller glial cells. The innermost layer contains retinal ganglion cells and some amacrine cells. Photoreceptors synapse onto interneurons, which process visual signals and pass them to retinal ganglion cells, which in turn send them to the rest of the brain. Most of the classes are divisible into discrete types—a total currently estimated at about 100—but well under half possess molecular markers that distinguish them specifically from other, related types. Drop-Seq provides an opportunity to identify molecular signatures of cell types previously defined exclusively by morphological or physiological criteria.

The retina presents formidable technical challenges for large-scale single cell profiling. First, about 70% of the cells in the retina are rod photoreceptors; the other retinal cell classes each comprise 0.5-8% of retinal cells and are further divided into types. The problem in the retina is therefore to identify a large number of individually rare cell types. Second, the size variation among retinal cells—ranging from 1.2 microns (rods) to 20 microns (retinal ganglion cells) in diameter and thus spanning three orders of magnitude in volume—can pose not only technical challenges for unbiased isolation of cells, but also complicate analysis because of huge cell-to-cell differences in mRNA content.

Applicants performed Drop-Seq on cell suspensions made from whole retinas of 14-day-old mice, sequencing 49,300 STAMPs to an average depth of 14,084 reads (STAMPs were collected in seven experimental batches over four days). To discover cell types from single-cell expression profiles ab initio, Applicants first performed principal components analysis, using the genes that showed a greater degree of expression variance (across cells) than could be explained by random statistical sampling of the transcripts (within cells), and initially focusing on the 13,155 cells with the largest numbers of transcripts, to reduce the otherwise-disproportionate contribution of tiny photoreceptor cells to the analysis (Experimental Procedures). Applicants utilized a classic permutation test (Peres-Neto et al., 2005) and a recently developed resampling procedure (Chung and Storey, 2014) to identify statistically significant principal components (PCs), finding 32 significant PCs in these data (FIG. 18 ). Almost all of the significant PCs were strongly shaped by genes that are well-known markers of retinal cell types. Applicants used the cell loadings associated with these principal components as input for t-Distributed Stochastic Neighbor Embedding (tSNE) (van der Maaten and Hinton, 2008), to reduce these 32 PCs to two dimensions. Applicants projected the remaining 36,145 cells in the data onto the tSNE, and combined a density clustering approach with differential expression analysis to identify distinct clusters of cells from this tSNE analysis (Extended Experimental Procedures). These steps left us with 39 transcriptionally distinct cell populations—the largest containing 29,400 cells, the smallest containing 50 cells, altogether composed of 44,808 cells (FIG. 11B). Finally, Applicants organized the 39 cell populations into larger categories (classes) of transcriptionally similar clusters, by building a dendrogram of similarity relationships among the 39 cell populations based upon their Euclidean distances in gene-expression space (FIG. 11D, left).

Applicants found that their unsupervised clustering results—which were derived entirely from clustering the single-cell transcriptome data itself, rather than being “instructed” by known markers—correlated strikingly with expression of the known molecular markers that exist for many retinal cell types (FIG. 11D, right). Well-known markers of retinal cell types include Slc17a6 (Vglut2) and Thy1 for retinal ganglion cells, Vsx2 for bipolar cells, Lhx1 for horizontal cells, opsins for photoreceptors, Tfap2b and Pax6 for amacrine cells, and Rlbp1 for Müller glia. Each of these markers showed single-cell patterns of gene expression that corresponded to a branch or leaf of the dendrogram derived from Applicants' unsupervised analysis (FIG. 11D). Photoreceptors clustered into two groups that were readily identifiable as rods and cones based on their expression of rod and cone opsins. Additional clusters corresponded to non-neural cells associated with retina, including astrocytes (associated with retinal ganglion cell axons exiting the retina), resident microglia (Provis et al., 1996), endothelial cells (from intra-retinal vasculature), pericytes (cells that surround the endothelium), and fibroblasts (FIG. 11D). Furthermore, Applicants found that the relative proportions of the major cell classes in Applicants' data largely agreed with earlier estimates from microscopy (Jeon et al., 1998). The ability of an unsupervised analysis to identify all of these biologically known cell classes at the expected ratios suggests that such analyses may be applicable to many other tissues whose resident cell populations are far less characterized.

Replication and cumulative power of Drop-Seq data. Replication across experimental sessions enables the construction of cumulatively more powerful datasets for detection of subtle biological signals. The retinal STAMPs were generated on four different days (weeks apart), utilizing four different mouse litters, with several sessions generating multiple replicate Drop-Seq runs, for a total of seven replicates. Applicants prepared one of these replicates at a particularly low cell concentration (15 cells/μl) and high purity, to evaluate whether any analytical results were artifacts of cell-cell doublets or single-cell impurity (i.e. whether they excluded these “high-purity” cells), as Drop-Seq's fastest-throughput modes allow extremely fast processing of living cells (valuable for maintaining correspondence to the in vivo system) but at some cost in single-cell purity relative to its highest-purity modes (FIG. 9A, 9B), and the correspondence between transcriptional patterns identified in these modes was important to understand. A key question, then, was whether every experimental session contributed cells to each of the 39 populations that Applicants had observed in the above analysis (FIG. 11B). Applicants found that all 39 clusters contained cells from every experimental session and condition. However, Cluster 36 (arrow in FIG. 11E; star in FIG. 20 ), drew disproportionately from replicates two and three. This cluster expresses markers of fibroblasts, a cell type that is not native to the retina but is instead present in tissue surrounding the retina; the inclusion of larger numbers of fibroblasts in two replicates most likely represents the challenge of dissecting around the retinal perimeter. Most importantly, the 3,226 cells prepared under high-purity conditions (replicate 7) contributed to every cluster, indicating that none of the clusters is an artifact of doublets or other impurities (FIG. 11E). While Applicants cannot exclude the possibility that experimental variation influences gene expression measurements in Drop-Seq, in these experiments such effects appeared to be small relative to the differences even between highly similar cell subtypes (e.g. the 21 populations of amacrines cells described below).

Applicants next examined how the classification of cells (based on their patterns of gene expression) evolved as a function of the numbers of cells in analysis, in order to evaluate both the robustness of the clustering analysis and the scientific return to analyzing large numbers of cells. Applicants used 500, 2,000, or 9,431 cells from Applicants' dataset, and asked how (for example) amacrine cells identified in the full (44,808-cell analysis) had clustered in analyses of smaller numbers of cells (FIG. 11F). Applicants found that as the number of cells in the data increased, distinctions between related clusters become clearer, stronger, and finer in resolution, with the result that a greater number of rare amacrine cell populations (each representing 0.1-0.9% of the cells in the experiment) could ultimately be distinguished from one another (FIG. 1F). In analyses of smaller numbers of cells, these cells were often co-clustered into “supertypes”, reflecting the challenge of distinguishing recurring patterns (often involving small numbers of genes) from single-cell biological, technical, and statistical noise in genome-wide experiments.

Profiles of 21 candidate amacrine cell types. To better understand the ability of single-cell analysis to distinguish between closely related cell types, Applicants focused on the 21 clusters identified as amacrine neurons, the neuronal class considered to be the most morphologically diverse (Masland, 2012). Most amacrine cells are inhibitory, with around half using glycine and the other half using GABA as a neurotransmitter. Excitatory amacrine cells, expressing Slc17a8 (VGlut3) and releasing glutamate, have also been identified (Haverkamp and Wassle, 2004). Another recently discovered amacrine cell population release no known classical neurotransmitter (nGnG amacrines) (Kay et al., 2011).

Applicants first identified potential amacrine markers that were the most universally expressed by amacrine clusters relative to other cell classes (FIG. 12A). Applicants then assessed the expression of known glycinergic and GABAergic markers; their mutually exclusive expression is seen as a fundamental distinction with a morphological correlate: most GABAergic amacrines have broad dendritic arbors restricted to a single sublamina (wide-field) whereas glycinergic amacrines have narrow dendritic arbors that span multiple sublaminae (narrow-field). Of the 21 clusters of amacrine cells, 12 groups (together comprising 2,516 cells) were identifiable as GABAergic and a distinct 5 clusters (together comprising 1,121 cells) as glycinergic, based on expression of the GABA synthetic enzyme, glutamate decarboxylase (two isoforms, encoded by Gad1 and Gad2) and the glycine transporter (Slc6a9), respectively (FIG. 12B). An additional cell population (comprising 73 cells) was identified as excitatory by its expression of Slc17a8, which was not expressed in other amacrine populations (FIG. 12B). The remaining three amacrine cell populations (clusters 4, 20, and 21) had absent or low levels of Gad1, Gad2, Slc6a9, and Slc17a8; these likely include nGnG amacrines, as described below.

The amacrine types with known molecular markers were readily assigned to specific cell populations (clusters) from the analysis. Glycinergic A-II amacrine neurons appeared to correspond to the most divergent glycinergic cluster (FIG. 12B, cluster 16), as this was the only cluster to strongly express the Gjd2 gene encoding the gap junction protein connexin 36 (Feigenspan et al., 2001; Mills et al., 2001). Ebf3, a transcription factor found in SEG glycinergic as well as nGnG amacrines, was specific to clusters 17 and 20. Starburst amacrine neurons (SACs), the only retinal cells that use acetylcholine as a co-transmitter, were identifiable as cluster 3 by those cells' expression of the choline acetyltransferase gene Chat (FIG. 12B); the Drop-Seq data also suggested that SACs, unlike the other GABAergic cells, expressed Gad1 but not Gad2, as previously observed in rabbit (Famiglietti and Sundquist, 2010).

Beyond the above distinctions, little is known about molecular distinctions among the physiologically and morphologically diverse amacrine types. Molecular markers of these types would be powerful tools for more comprehensively studying amacrines' circuitry, development, and function. For each of the 21 amacrine cell populations (clusters), Applicants identified multiple genes that were highly enriched in each cluster relative to the other amacrines (FIG. 12C). Many markers of each cluster (FIG. 12C) are genes involved in neurotransmission or neuromodulation; such genes have historically been good markers of individual neuronal cell types in other brain regions.

Can Drop-Seq identify novel markers of cell types? Applicants analyzed genes expressed in two of the amacrine clusters: cluster 7, a GABAergic cluster, and cluster 20, which had a mixture of glycinergic and nGnG cells. First, Applicants co-stained retinal sections with antibodies to the transcription factor MAF, the top marker of cluster 7, plus antibodies to either GAD1 or SLC6A9, markers of GABAergic and glycinergic transmission, respectively. As predicted by Drop-Seq data, MAF was found specifically in a small subset of amacrine cells that were GABAergic and not glycinergic (FIG. 12D). Cluster 7 had numerous genes that were enriched relative to its nearest neighbor, cluster 6 (FIG. 12E, 16 genes>2.8-fold enrichment, p<10⁻⁹), including Crybb3, which belongs to the crystallin family of proteins that are known to be directly upregulated by Maf during ocular lens development (Yang and Cvekl, 2005), and another, the matrix metalloproteinase Mmp9, that has been shown to accept crystallins as a substrate (Descamps et al., 2005; Starckx et al., 2003). Second, Applicants stained sections with antibodies to PPP1R17, which was selectively expressed in cluster 20. Cluster 20 shows weak, infrequent glycine transporter expression and is one of only two clusters (with cluster 21) that express Neurod6, a marker of nGnG neurons (Kay et al., 2011), which are neither glycinergic nor GABAergic. Applicants used a transgenic strain (MitoP) that has been shown to express cyan fluorescent protein (CFP) specifically in nGnG amacrines (Kay et al., 2011). PPP1R17 stained in 85% of all CFP-positive amacrines in the MitoP line, validating this as a marker of nGnG cells. The absence of PPP1R17 from putative nGnG amacrines in Cluster 21 suggests a hitherto unsuspected level of heterogeneity among nGnG amacrines. Like cluster 7, cluster 20 expressed numerous markers distinguishing it from its closest neighbor (FIG. 12G; 12 genes >2.8-fold enrichment, p<10-9).

Identification of additional cellular diversity within individual clusters. Applicants' unsupervised clustering analysis grouped cells into 39 distinct populations; as many as 100 retinal cell types are proposed to exist based on morphology or physiology. Applicants therefore asked whether additional heterogeneity and population structure might exist within clusters and be visible in supervised analyses; this would suggest that still-deeper classification will become possible with larger numbers of cells, or with combinations of unsupervised and known-marker-driven analyses. Here Applicants focus on cone photoreceptors and retinal ganglion cells.

Cones. Mice are dichromats, having only short-wavelength (blue or S-) and middle-wavelength (green or M-) opsins, encoded by the genes Opn1sw and Opn1mw, respectively. The S- and M-opsins are expressed in opposing gradients along the dorsal-ventral axis, with many cones, especially in central retina, expressing both of these opsins (Szel et al., 2000). No other genes have been identified that selectively mark S- or M-cones.

Applicants identified cluster 25 as cones by their expression of Opn1mw, Opn1sw, Arr3, and other cone-specific genes. Applicants compared genome-wide gene expression in 336 cells (in cluster 25) expressing only Opn1sw (the blue-light-sensitive opsin) to expression in 551 cells (in the same cluster) expressing only Opn1mw (the green-light-sensitive opsin) (FIG. 12H). Eight genes differed in expression by at least 2-fold (and at p<10⁻⁹) between the two cell populations. One such gene, Thrb, encodes the receptor for thyroid hormone, a key developmental regulator of the dorsal-ventral patterning that shapes differential opsin expression (Roberts et al., 2006). Two other genes, Smug1 and Ccdc136, have been shown to be concentrated in dorsal and ventral cones respectively (Corbo et al., 2007), consistent with Applicants' assignment of them to M- and S-cones.

Retinal ganglion cells. Retinal ganglion cells (RGCs), the sole output neuron class from the retina, are believed to consist of about 20 types, of which several have known molecular markers (Masland and Sanes, 2015). RGCs altogether comprise less than 1% of the cells in the retina (Jeon et al., 1998). In Applicants' analysis of 44,808 cells, Applicants identified a single RGC cluster, consisting of less than 1% of all cells analyzed. Opn4, the gene encoding melanopsin, is a known marker of a distinct RGC type (Hattar et al., 2002); among the 432 RGCs, Applicants identified 26 cells expressing Opn4. These 26 cells expressed seven genes at least two-fold more strongly than the 406 Opn4-RGCs did (p<109, FIG. 12I); one of these seven genes was Eomes, recently shown to be required for development and maintenance of melanopsin-containing RGCs (Mao et al., 2014).

Human bone marrow cells. Human bone marrow cells contain multipotent haematopoietic stem cells which differentiate into two types of progenitors: lymphoid stem cells and myeloid stem cells. Lymphoid stem cells differentiate to prolymphocytes which develop into T, B and NK cells (i.e., peripheral blood mononuclear cells), while myeloid stem cells differentiate into three types of cell lines: granulocyte-monocyte progenitors, erythroid progenitors, and megakaryocytes. Peripheral blood mononuclear cells (PBMCs) consist of blood cells with a round nucleus which are involved in fighting diseases such as leukemias, cancers, and infectious diseases. Applicants' analysis of 471 single-cell transcription profiles prepared by Drop-Seq identified 8 clusters of gene markers which correlated to known cell types of haematopoietic stem cells.

Discussion

Here Applicants have described Drop-Seq, a new technology for simultaneously analyzing genome-wide expression in unconstrained numbers of individual cells. Applicants first validated Drop-Seq by profiling mixtures of intact human and mouse cells. Applicants then used Drop-Seq to ascertain cell states in a nominally homogeneous cell population and cell types in a complex tissue. To analyze cell states, Applicants profiled the cell cycle at near-continuous temporal resolution across 1,001 asynchronously growing cells from two species, uncovering novel cell cycle-regulated genes with evolutionarily conserved expression oscillations. To analyze cell types, Applicants profiled 44,808 individual cells from the mouse retina, an accessible portion of the central nervous system. Applicants identified 39 transcriptionally distinct cell populations in the retina, revealed novel relationships among those cells, and nominated new cell type-specific markers, two of which Applicants validated by immunohistochemistry.

In other embodiments of the technology, the application of the technology can be used to identify novel biomarkers of a disease, such as cancer or an autoimmune disease, by identifying cell populations, cell markers, or combinations of cell populations, that are specifically present in a disease state versus a healthy state.

In a further application, the Drop-Seq technology can be applied to disease modeling or prognosticating disease. The single-cell technique can be utilized to diagnose diseases with unclear etiologies or origins. For example, cancer of unknown primary tissue could be traced to a tissue-of-origin by identifying rare cells in the tissue that express markers of a cell-type of a particular tissue.

As discussed above, the Drop-Seq process generates STAMPs (single-cell transcriptomes attached to microparticles). Hence, the microparticle has a stable record of the mRNAs present in a cell and therefore can be probed for expression of different genes. For example, since the Drop-Seq technology can be utilized to rapidly sequence genes in parallel, it would be possible to probe those genes associated with a phenotype difference in microbiomes associated with human bodies. The technology can therefore be extended to analyze molecules, organelles, cellular fragments (e.g., synapses), whole cells, or collection of cells (i.e., organoids).

To become widely adopted, and to advance biology, a new technology should possess these characteristics:

1. It should fill an unmet scientific need. Biologists are quickly recognizing the scientific opportunities enabled by ascertaining transcriptional variation at the cellular level. Current methods, however, can profile only up to a few hundred cells per day, at a cost of $3-$50 per cell. By contrast, a single scientist employing Drop-Seq can completely prepare 10,000 single-cell libraries for sequencing, for about 6 cents per cell. Applicants hope that ease, speed, and low cost facilitate exuberant experimentation, careful replication, and many cycles of experiments, analyses, ideas, and more experiments.

2. It should be easy to adopt. The simpler a technology, the greater the likelihood that it can be adopted by the scientists who will know how to put it to good use. Drop-Seq utilizes equipment that is available to any biology lab—a small inverted microscope and syringe pumps such as those routinely used for microinjection. A Drop-Seq setup can be constructed quickly and inexpensively (FIG. 21 and Extended Experimental Procedures). Drop-Seq also uses two novel reagents: the microfluidic devices for droplet preparation, and the beads to individually barcode each cell's RNA. Applicants designed the microfluidics devices (through 30 design iterations) to be simple, passive devices that could be readily constructed in any academic or commercial microfluidics facility, and Applicants provide a CAD file to enable this. The barcoded beads described here will be available upon the publication of this paper (Extended Experimental Procedures). Applicants' supplemental materials include detailed protocols for interested readers.

3. It should be thoroughly tested to provide a clear understanding of the technology's advantages and limitations. Here Applicants used mixtures of mouse and human cells to carefully measure both single-cell purity and the frequency of cell doublets—the first work that Applicants are aware of to test any single-cell analysis strategy in this way. Applicants find that Applicants can tune two key quality parameters—cell-cell doublets and contaminating RNA—by adjusting the input cell concentration, and that at lower cell concentrations (still accommodating a throughput of 1,200 cells per hour) Drop-Seq compares favorably to existing technology for both doublets and purity. Applicants' results suggest that other methods of isolating single cells from a cell suspension, such as fluorescence activated cell sorting (FACS) or microfluidics, are also vulnerable to doublets and single-cell impurities. The analysis of Applicants' retina dataset suggests that even relatively impure libraries generated in “ultra-high-throughput” modes (100 cells per μl, allowing the processing of 10,000 cells per hour at ˜10% doublet and impurity rates) can yield a rich, robust and biologically validated cell classification, but other tissues or applications may require using purer modes of Drop-Seq. Applicants would always suggest that pilot analyses begin with one of Drop-Seq's higher-purity modes.

The other major quality metric of a single-cell profiling technology is capture efficiency. Applicants estimated Drop-Seq's capture efficiency to be about 12%, based on analyses of synthetic RNA “spike-ins,” which Applicants then corroborated by highly sensitive digital PCR measurements of ten genes. Studies of single-cell digital expression profiling methods in the past year have reported capture rates of 3%, 3.4%, and 48%, though these rates have not been estimated or corrected in uniform ways; Applicants chose a particularly conservative estimation method to arrive at the 12% estimate for Drop-Seq and suggest that a great need in single-cell genomics is for uniform comparison strategies and metrics. Applicants' analysis of the retina indicates that capturing only ˜12% of each cell's transcriptome (and sequencing less than that) may allow even subtle cell type differences (e.g. among 21 amacrine cell populations) to be recognized; this extends an idea proposed in a recent study of 301 cortical cells (Pollen et al., 2014). The ability to analyze so many cells may help to elucidate biological patterns that would otherwise be elusive, as these patterns are then shared across large numbers of analyzed cells in ways that overwhelm the biological, technical and statistical-sampling noise that exists at the single-cell level.

Unsupervised computational analysis of Drop-Seq data identified 39 transcriptionally distinct retinal cell populations; all turned out to belong to known cell classes, and most appeared to correspond to known or hypothesized retinal cell types and subtypes, based on expression of previously validated markers (FIGS. 11 and 12 ). It is a particular strength of the retina that establishing correspondence between cluster and type was in many cases straightforward; classification has not proceeded sufficiently far in most other parts of the brain to permit such validation, which is why initial validation in a tissue like the retina was so important. Many of these cell populations—especially those within the amacrine class—nominated new distinguishing markers for cells previously identified only by morphology and physiology.

Many interesting questions surround the definition of cell types from transcriptomics data. For example, are there always clear expression thresholds beyond which two groups of cells are distinct types, or are distinctions sometimes graded and continuous? More importantly, how do transcriptional differences among cell populations give rise to anatomical and physiological differences? The throughput afforded by Drop-Seq may enable such questions to be comprehensively addressed in whole tissues, by providing sufficient numbers of profiles to appreciate patterns of expression even in rare cell types.

Applicants see many other important applications of Drop-Seq in biology, beyond the identification of cell types and cell states. Genome-scale genetic studies are identifying large numbers of genes in which genetic variation contributes to disease risk; but biology has lacked similarly high-throughput ways of connecting genes to specific cell populations and their unique functional responses. Finding the cellular sites and biological activities of so many genes will be important for going from genetic leads to biological insights. High-throughput single-cell transcriptomics could localize the expression of risk genes to specific cell types, and in conjunction with genetic perturbations, could also help to systematically relate each gene to (i) the cell types most affected by loss or perturbation of those genes; and (ii) the alterations in cell state elicited by such perturbations. Such approaches could help cross the daunting gap from high-throughput gene discovery to (harder-to-acquire) real insights about the etiology of human diseases (McCarroll et al., 2014).

The coupling of Drop-Seq to additional perturbations—such as small molecules, mutations (natural or engineered), pathogens, or other stimuli—could be used to generate an information-rich, multi-dimensional readout of the influence of perturbations on many kinds of cells. When studying the effects of a mutation, for example, Drop-Seq could simultaneously reveal the ways in which the same mutation impacts many cell types in both cell-autonomous and cell-nonautonomous ways.

The functional implications of a gene's expression are a product not just of the gene or encoded protein's intrinsic properties, but also of the entire cell-level context in which the gene is expressed. Applicants hope Drop-Seq will enable the abundant and routine discovery of such relationships in many areas of biology.

Experimental Procedures

Device fabrication. Microfluidic devices were designed using AutoCAD software (Autodesk, Inc.), and the components tested using COMSOL Multiphysics (COMSOL Inc.). A CAD file is also available in the supplement.

Devices were fabricated using a bio-compatible, silicon-based polymer, polydimethylsiloxane (PDMS) via replica molding using the epoxy-based photo resist SU8 as the master, as previously described (Mazutis et al., 2013; McDonald et al., 2000). The PDMS devices were then rendered hydrophobic by flowing in Aquapel (Rider, Mass., USA) through the channels, drying out the excess fluid by flowing in pressurized air, and baking the device at 65° C. for 10 minutes.

Barcoded microparticle synthesis. Bead functionalization and reverse direction phosphoramidite synthesis were performed by Chemgenes Corp (Wilmington, Mass.). “Split-and-pool” cycles were accomplished by removing the dry resin from each column, hand mixing, and weighing out four equal portions before returning the resin for an additional cycle of synthesis. Full details (including availability of the beads) are described in Extended Experimental Procedures.

Drop-Seq procedure. A complete, in-depth description of the protocol, including the composition and catalogue numbers for all reagents, can be found in Extended Experimental Procedures. In brief, droplets ˜1 nL in size were generated using the co-flow microfluidic device described above, in which barcoded microparticles, suspended in lysis buffer, were flowed at a rate equal to that of a single-cell suspension, so that the droplets were composed of an equal amount of each component. As soon as droplet generation was complete, droplets were broken with perfluorooctanol in 30 mL of 6×SSC. The addition of a large aqueous volume to the droplets reduces hybridization events after droplet breakage, because DNA base pairing follows second-order kinetics (Britten and Kohne, 1968; Wetmur and Davidson, 1968). The beads were then washed and resuspended in a reverse transcriptase mix. After incubation for 30 min at 25° C. and 90 min at 42° C., the beads were washed and resuspended in Exonuclease I mix and incubated for 45 min at 37° C. The beads were washed, counted, aliquoted into PCR tubes, and PCR amplified (see Extended Experimental Procedures for details). The PCR reactions were purified and pooled, and the amplified cDNA quantified on a BioAnalysis High Sensitivity Chip (Agilent). The 3′-ends were fragmented and amplified for sequencing using the Nextera XT DNA sample prep kit (Illumina) using custom primers that enabled the specific amplification of only the 3′ ends (Table 9). The libraries were purified and quantitated on a High Sensitivity Chip, and sequenced on the Illumina NextSeq 500. All details regarding reaction conditions, primers used, and sequencing specifications can be found in the Extended Experimental Procedures.

Alignment and estimation of digital expression levels. Raw sequence data was filtered, adapter- and polyA-trimmed, and aligned to either the mouse (mm10) genome for retina experiments, or a combined mouse (mm10)-human (hg19) mega-reference, using STAR v2.4.0 (Dobin et al., 2013). All reads with the same cell barcode were grouped together, and reads from the same cell aligning to the same gene, with UMIs within ED=1, were merged. On each cell, for each gene, the unique UMIs were counted; this count was then placed into a digital expression matrix. The matrix was ordered by the sum of all UMIs per cell, and a cumulative sum plot was generated. Applicants determined the number of STAMPs by estimating the first inflection point (FIG. 14B), which Applicants empirically found to always be close to the estimated number of amplified STAMPs. Additional details can be found in Extended Experimental Procedures.

Cell cycle analysis of HEK and 3T3 cells. Gene sets reflecting five phases of the HeLa cell cycle (G1/S, S, G2/M, M and M/G1) were taken from Whitfield et al. (Whitfield et al., 2002), with some modification (Extended Experimental Procedures). A phase-specific score was generated for each cell, across all five phases, using averaged normalized expression levels (log₂ (TPM+1)) of the genes in each gene set. Cells were then ordered along the cell cycle by comparing the patterns of these five phase scores per cell. To identify cell cycle-regulated genes, Applicants used a sliding window approach, and identified windows of maximal and minimal average expression, both for ordered cells, and for shuffled cells, to evaluate the false-discovery rate. Full details may be found in Extended Experimental Procedures.

Generation of whole retina suspension. Suspensions were prepared from the retinas of 14-day-old (P14) C57BL/6 mice by adapting previously described methods (Barres et al., 1988). See Extended Experimental Procedures for additional details.

Principal components and clustering analysis of retina data. Principal components analysis (PCA) was first performed on a 13,155-cell “training set” of the 49,300-cell dataset, using single-cell libraries with >900 genes. Applicants found their approach was more effective in discovering structures corresponding to rare cell types than performing PCA on the full dataset, which was dominated by numerous, tiny rod photoreceptors (Extended Experimental Procedures). 384 genes that showed either significant variability or structure within this training set were used to learn the principal components (PCs). Thirty-two statistically significant PCs were identified using a permutation test and independently confirmed using a modified resampling procedure (Chung and Storey, 2014). To visualize the organization of cell-types in the retina, Applicants projected individual cells within the training set based on their scores along the significant PCs onto a single two-dimensional map using t-Distributed Stochastic Neighbor Embedding (t-SNE) (van der Maaten and Hinton, 2008). The remaining 36,145 single-cell libraries (<900 genes detected) were next projected on to this t-SNE map, based on their representation within the PC-subspace of the training set (Berman et al., 2014; Shekhar et al., 2014). This approach mitigates the impact of noisy variation in the lower complexity libraries due to gene dropouts, and was also reliable in the sense that when Applicants withheld from the tSNE all cells from a given cluster and then tried to project them, these withheld cells were not spuriously assigned to another cluster by the projection (Table 10). Furthermore, cells are not allowed to be projected based on similarity to less than 10 cells (see Extended Experimental Procedures). Point clouds on the t-SNE map represent cell-types, and density clustering (Ester et al., 1996) identified these regions, using two sets of parameters for defining both large and small clusters. Differential expression testing (McDavid et al., 2013) was then used to confirm that clusters were distinct from each other. Hierarchical clustering based on Euclidean distance and complete linkage was used to build a tree relating the clusters. Applicants noted expression of several rod-specific genes, such as Rho and Nrl, in every cell cluster, an observation that has been made in another retinal cell gene expression study (Siegert et al., 2012). This likely arises from solubilization of these high-abundance transcripts during cell suspension preparation. Additional information regarding retinal cell data analysis can be found in the Extended Experimental Procedures.

Example 3: Extended Experimental Procedures for Example 2

Bead Synthesis. Bead functionalization and reverse direction phosphoramidite synthesis (5′ to 3′) were performed by Chemgenes Corp. Toyopearl HW-65S resin (30 micron mean particle diameter) was purchased from Tosoh Biosciences, and surface alcohols were functionalized with a PEG derivative to generate an 18-carbon long, flexible-chain linker. The functionalized bead was then used as a solid support for reverse direction phosphoramidite synthesis (5′→3′) on an Expedite 8909 DNA/RNA synthesizer using DNA Synthesis at 10 micromole cycle scale and a coupling time of 3 minutes. Amidites used were: N-Benzoyl-3′-O-DMT-2′-deoxy adenosine-5′-cyanoethyl-N,N-diisopropyl-phosphoramidite (dA-N⁶-Bz-CEP); N⁴-Acetyl-3′-O-DMT-2′-deoxy-cytidine-5′-cyanoethyl-N,N-diisopropyl-phosphoramidite (dC-N⁴-Ac-CEP); N²-DMF-3′-O-DMT-2′-deoxy guanosine-5′-cyanoethyl-N,N-diisopropyl-phosphoramidite (dG-N²-DMF-CEP); and 3′-O-DMT-2′-deoxy thymidine-5′-cyanoethyl-N,N-diisopropyl-phosphoramidite (T-CEP). Acetic anhydride and N-methylimidazole were used in the capping step; ethylthio-tetrazole was used in the activation step; iodine was used in the oxidation step, and dichloroacetic acid was used in the deblocking step. After each of the twelve split-and-pool phosphoramidite synthesis cycles, beads were removed from the synthesis column, pooled, hand-mixed, and apportioned into four equal portions by mass; these bead aliquots were then placed in a separate synthesis column and reacted with either dG, dC, dT, or dA phosphoramidite. This process was repeated 12 times for a total of 4{circumflex over ( )}12=16,777,216 unique barcode sequences. For complete details regarding the barcoded bead sequences used.

Cell Culture. Human 293 T cells were purchased as well as the murine NIH/3T3 cells. 293T and 3T3 cells were grown in DMEM supplemented with 10% FBS and 1% penicillin-streptomycin.

Cells were grown to a confluence of 30-60% and treated with TrypLE for five min, quenched with equal volume of growth medium, and spun down at 300×g for 5 min. The supernatant was removed, and cells were resuspended in 1 mL of 1×PBS+0.2% BSA and re-spun at 300×g for 3 min. The supernatant was again removed, and the cells re-suspended in 1 mL of 1×PBS, passed through a 40-micron cell strainer and counted. For Drop-Seq, cells were diluted to the final concentration in 1×PBS+200 μg/mL BSA.

Generation of Whole Retina Suspensions. Single cell suspensions were prepared from P14/mouse retinas by adapting previously described methods for purifying retinal ganglion cells from rat retina (Barres et al., 1988). Briefly, mouse retinas were digested in a papain solution (40U papain/10 mL DPBS) for 45 minutes. Papain was then neutralized in a trypsin inhibitor solution (0.15% ovomucoid in DPBS) and the tissue was triturated to generate a single cell suspension. Following trituration, the cells were pelleted and resuspended and the cell suspension was filtered through a 20 μm Nitex mesh filter to eliminate any clumped cells and this suspension was then used for Drop-Seq. The cells were then diluted in DPBS+0.2% BSA to either 200 cells/μl (replicates 1-6) or 30 cells/μl (replicate 7).

Retina suspensions were processed through Drop-Seq on four separate days. One library was prepared on day 1 (replicate 1); two libraries on day 2 (replicates 2 and 3); three libraries on day 3 (replicates 4-6); and one library on day 4 (replicate 7, high purity). To replicates 4-6, human HEK cells were spiked in at a concentration of 1 cell/μl (0.5%) but the wide range of cell sizes in the retina data made it impossible to calibrate single-cell purity or doublets using the cross-species comparison method. Each of the seven replicates was sequenced separately.

Drop-Seq

Preparation of beads. Beads (either Barcoded Bead SeqA or Barcoded Bead SeqB; Table 9 and see note at end of Extended Experimental Procedures) were washed twice with 30 mL of 100% EtOH and twice with 30 mL of TE/TW (10 mM Tris pH 8.0, 1 mM EDTA, 0.01% Tween). The bead pellet was resuspended in 10 mL TE/TW and passed through a 100 μm filter into a 50 mL Falcon tube for long-term storage at 4° C. The stock concentration of beads (in beads/μL) was assessed using a Fuchs-Rosenthal cell counter purchased from INCYTO. For Drop-Seq, an aliquot of beads was removed from the stock tube, washed in 500 μL of Drop-Seq Lysis Buffer (DLB, 200 mM Tris pH 7.5, 6% Ficoll PM-400, 0.2% Sarkosyl, 20 mM EDTA), then resuspended in the appropriate volume of DLB+50 mM DTT for a bead concentration of 100 beads/μL.

Droplet generation. The two aqueous suspensions—the single-cell suspension and the bead suspension—were loaded into 3 mL plastic syringes containing a 6.4 mm magnetic stir disc. Droplet generation oil was loaded into a 10 mL plastic syringe. The three syringes were connected to a 125 μm coflow device (FIG. 15A) by 0.38 mm inner-diameter polyethylene tubing, and injected using syringe pumps at flow rates of 4.1 mL/hr for each aqueous suspension, and 14 mL/hr for the oil, resulting in ˜125 μm emulsion drops with a volume of ˜1 nanoliter each. For movie generation, the flow was visualized under an optical microscope at 10× magnification and imaged at ˜1000-2000 frames per second using a FASTCAM SA5 color camera. Droplets were collected in 50 mL falcon tubes; the collection tube was changed out for every 1 mL of combined aqueous flow volume to reduce the amount of soluble RNA in solution upon droplet breakage.

During droplet generation, the beads were kept in suspension by continuous, gentle magnetic stirring. The uniformity in droplet size and the occupancy of beads were evaluated by observing aliquots of droplets under an optical microscope with bright-field illumination; in each experiment, greater than 95% of the bead-occupied droplets contained only a single bead.

Droplet breakage. The oil from the bottom of each aliquot of droplets was removed with a P1000 pipette, after which 30 mL 6×SSC at room temperature was added. To break droplets, Applicants added 600 μL of Perfluoro-1-octanol, and shook the tube vigorously by hand for about 20 seconds. The tube was then centrifuged for 1 minute at 1000×g. To reduce the likelihood of annealed mRNAs dissociating from the beads, the sample was kept on ice for the remainder of the breakage protocol. The supernatant was removed to roughly 5 mL above the oil-aqueous interface, and the beads washed with an additional 30 mL of room temperature 6×SSC, the aqueous layer transferred to a new tube, and centrifuged again. The supernatant was removed, and the bead pellet transferred to non-stick 1.5 mL microcentrifuge tubes. The pellet was then washed twice with 1 mL of room temperature 6×SSC, and once with 300 μL of 5× Maxima H-RT buffer (EP0751).

Reverse transcription and Exonuclease I treatment. To a pellet of 90,000 beads, 200 μL of RT mix was added, where the RT mix contained 1× Maxima RT buffer, 4% Ficoll PM-400, 1 mM dNTPs, 1 U/μL Rnase Inhibitor, 2.5 μM Template_Switch_Oligo (Table 9), and 110U/μL Maxima H-RT. Ficoll was included to reduce settling, and because of its ability to improve RT efficiency (Lareu et al., 2007). The beads were incubated at room temperature for 30 minutes, followed by 42° C. for 90 minutes. The beads were then washed once with 1 mL 1×TE+0.5% Sodium Dodecyl Sulfate, twice with 1 mL TE/TW, and once with 10 mM Tris pH 7.5. The bead pellet was then resuspended in 200 μL of exonuclease I mix containing 1× Exonuclease I Buffer and 1 U/μL Exonuclease I, and incubated at 37° C. for 45 minutes.

The beads were then washed once with 1 mL TE/SDS, twice with 1 mL TE/TW, once with 1 mL ddH₂O, and resuspended in ddH₂O. Bead concentration was determined using a Fuchs-Rosenthal cell counter. Aliquots of 1000 beads were amplified by PCR in a volume of 50 μL using 1× Hifi HotStart Readymix and 0.8 μM Template_Switch_PCR primer (Table 9).

The aliquots were thermocycled as follows: 95° C. 3 min; then four cycles of: 98° C. for 20 sec, 65° C. for 45 sec, 72° C. for 3 min; then X cycles of: 98° C. for 20 sec, 67° C. for 20 sec, 72° C. for 3 min; then a final extension step of 5 min. For the human-mouse experiment using cultured cells, X was 8 cycles; for the dissociated retina experiment, X was 9 cycles. Pairs of aliquots were pooled together after PCR and purified with 0.6× Agencourt AMPure XP beads according to the manufacturer's instructions, and eluted in 10 μL of H₂O. Aliquots were pooled according to the number of STAMPs to be sequenced, and the concentration of the pool quantified on a BioAnalyzer High Sensitivity Chip.

Preparation of Drop-Seq cDNA library for sequencing. To prepare 3′-end cDNA fragments for sequencing, four aliquots of 600 pg of cDNA of each sample was used as input in standard Nextera XT tagmentation reactions, performed according to the manufacturer's instructions except that 200 nM of the custom primers P5_TSO_Hybrid and Nextera_N701 (Table 9) were used in place of the kit's provided oligonucleotides. The samples were then amplified as follows: 95° C. for 30 sec; 11 cycles of 95° C. for 10 sec, 55° C. for 30 sec, 72° C. for 30 sec; then a final extension step of 72° C. for 5 min.

Pairs of the 4 aliquots were pooled together, and then purified using 0.6× Agencourt AMPure XP Beads according to the manufacturer's instructions, and eluted in 10 μL of water. The two 10 μL aliquots were combined together and the concentration determined using aBioAnalyzer High Sensitivity Chip. The average size of sequenced libraries was between 450 and 650 bp.

The libraries were sequenced on the Illumina NextSeq, using 4.67 μM in a volume of 3 mL HT1, and 3 mL of 0.3 μM Read1CustSeqA or Read1CustSeqB (Table 9 and see note at the end of Extended Experimental Procedures) for priming of read 1. Read 1 was 20 bp (bases 1-12 cell barcode, bases 13-20 UMI); read 2 (paired end) was 50 bp for the human-mouse experiment, and 60 bp for the retina experiment.

Species contamination experiment. To determine the origin of off-species contamination of STAMP libraries (FIG. 15E), Applicants: (1) performed Drop-Seq exactly as above (control experiment) with a HEK/3T3 cell suspension mixture of 100 cells/μL in concentration; (2) performed microfluidic co-flow step with HEK and 3T3 cells separately, each at a concentration of 100 cells/μL, and then mixed droplets prior to breakage; and (3) performed STAMP generation through exonuclease digestion, with the HEK and 3T3 cells separately, then mixed equal numbers of STAMPs prior to PCR amplification. A single 1000 microparticle aliquot was amplified for each of the three conditions, then purified and quantified on a BioAnalyzer High Sensitivity DNA chip. 600 pg of each library was used in a single Nextera Tagmentation reaction as described above, except that each of the three libraries was individually barcoded with the primers Nextera_N701 (condition 1), Nextera_N702 (condition 2), or Nextera_N703 (condition 3), and a total of 12 PCR cycles were used in the Nextera PCR instead of 11. The resulting library was quantified on a High Sensitivity DNA chip, and run at a concentration of 25 μM on the MiSeq using 0.5 μM Read1CustSeqA as a custom primer for read1.

Soluble RNA experiments. To quantify the number of primer annealing sites, 20,000 beads were incubated with 10 μM of polyadenylated synthetic RNA (synRNA, Table 9) in 2×SSC for 5 min at room temperature, and washed three times with 200 μL of TE-TW, then resuspended in 10 μL of TE-TW. The beads were then incubated at 65° C. for 5 minutes, and 1 μL of supernatant was removed for spectrophotometric analysis on the Nanodrop 2000. The concentration was compared with beads that had been treated the same way, except no synRNA was added.

To determine whether the bead-bound primers were capable of reverse transcription, and to measure the homogeneity of the cell barcode sequence on the bead surface, beads were washed with TE-TW, and added at a concentration of 100/μL to the reverse transcriptase mix described above. This mix was then co-flowed into the standard Drop-Seq 120-micron co-flow device with 200 nM SynRNA in 1×PBS+0.02% BSA. Droplets were collected and incubated at 42° C. for 30 minutes. 150 μL of 50 mM EDTA was added to the emulsion, followed by 12 μL of perfluooctanoic acid to break the emulsion. The beads were washed twice in 1 mL TE-TW, followed by one wash in H₂O, then resuspended in TE. Eleven beads were handpicked under a microscope into a 50 μL PCR mix containing 1×Kapa HiFi Hotstart PCR mastermix, 400 nMP7-TSO_Hybrid, and 400 nM TruSeq_F (Table 9). The PCR reaction was cycled as follows: 98° C. for 3 min; 12 cycles of: 98° C. for 20 s, 70° C. for 15 s, 72° C. for 1 min; then a final 72° C. incubation for 5 min. The resulting amplicon was purified on a Zymo DNA Clean and Concentrator 5 column, and run on a BioAnalyzer High Sensitivity Chip to estimate concentration. The amplicon was then diluted to 2 nM and sequenced on an Illumina MiSeq. Read 1, primed using the standard Illumina TruSeq primer, was a 20 bp molecular barcode on the SynRNA, while Read 2, primed with CustSynRNASeq, contained the 12 bp cell barcode and 8 bp UMI.

To estimate the efficiency of Drop-Seq, Applicants used a set of external RNAs. Applicants diluted the ERCC spike-ins to 0.32% of the stock in 1×PBS+1 U/μL RNase Inhibitor+200 μg/mL BSA (NEB), and used this in place of the cell flow in the Drop-Seq protocol, so that each bead was incubated with ˜100,000 ERCC mRNA molecules per nanoliter droplet. Sequence reads were aligned to a dual ERCC-human (hg19) reference, using the human sequence as “bait,” which dramatically reduced the number of low-quality alignments to ERCC transcripts reported by STAR compared with alignment to an ERCC-only reference.

Standard mRNA-seq. To compare Drop-Seq average expression data to standard mRNAseq data, Applicants used 1.815 ug of purified RNA from 3T3 cells, from which Applicants also prepared and sequenced 550 STAMPs. The RNA was used in the TruSeq Stranded mRNA Sample Preparation kit according to the manufacturer's instructions. For NextSeq 500 sequencing, 0.72 μM of Drop-Seq library was combined with 0.48 μM of the mRNAseq library.

In-solution template switch amplification. To compare Drop-Seq average expression data to mRNAseq libraries prepared by a standard, in-solution template switch amplification approach, 5 ng of purified RNA from 3T3 cells, from which Applicants also prepared and sequenced 550 STAMPs, was diluted in 2.75 μl of H₂O. To the RNA, 1 μl of 10 μM UMI_SMARTdT primer was added (Table 9) and heated to 72° C., followed by incubation at 4° C. for 1 min, after which Applicants added 2 μl 20% Ficoll PM-400, 2 μl 5×RT Buffer (Maxima H-kit), 1 μl 10 mM dNTPs, 0.5 μl 50 μM Template_Switch_Oligo (Table 9), and 0.5 μl Maxima H-RT. The RT was incubated at 42° C. for 90 minutes, followed by heat inactivation for 5 min at 85° C. An RNase cocktail (0.5 μl RNase I, Epicentre N6901K, and 0.5 μl RNase H) was added to remove the terminal riboGs from the template switch oligo, and the sample incubated for 30 min at 37° C. Then, 0.4 μl of M Template_Switch_PCR primer was added, along with 25 μl 2× Kapa Hifi supermix, and 13.6 μl H₂O. The sample was cycled as follows: 95° C. 3 min; 14 cycles of: 98° C. 20 s, 67° C. 20 s, and 72° C. 3 min; then 72° C. 5 min. The samples were purified with 0.6 AMPure XP beads according to the manufacturer's instructions, and eluted in 10 μl. 600 μg of amplified cDNA was used as input into a Nextera XT reaction. 0.6 μM of library was sequenced on a NextSeq 500, multiplexed with three other samples; Read1CustSeqB was used to prime read 1.

Droplet digital PCR (ddPCR) experiments. To quantify the efficiency of Drop-Seq, 50,000 HEK cells, prepared in an identical fashion as in Drop-Seq, were pelleted and RNA purified using the Qiagen RNeasy Plus Kit according to the manufacturer's protocol. The eluted RNA was diluted to a final concentration of 1 cell-equivalent per microliter in an RT-ddPCR reaction containing RT-ddPCR supermix, and a gene primer-probe set. Droplets were produced using BioRad ddPCR droplet generation system, and thermocycled with the manufacturer's recommended protocol, and droplet fluorescence analyzed on the BioRad QX100 droplet reader. Concentrations of RNA and confidence intervals were computed by BioRad QuantaSoft software. Three replicates of 50,000 HEK cells were purified in parallel, and the concentration of each gene in each replicate was measured two independent times. The probes used were: ACTB (hs01060665_g1), B2M (hs00984230_m1), CCNB1 (mm03053893), EEF2 (hs00157330_m1), ENO1 (hs00361415_m1), GAPDH (hs02758991_g1), PSMB4 (hs01123843_g1), TOP2A (hs01032137_m1), YBX3 (hs01124964_m1), and YWHAH (hs00607046_m1).

To estimate the RNA hybridization efficiency of Drop-Seq, human brain total RNA was diluted to 40 ng/μl in a volume of 20 μl and combined with 20 μl of barcoded primer beads resuspended in Drop-Seq lysis buffer (DLB, composition shown below) at a concentration of 2,000 beads/μl. The solution was incubated at 15 minutes with rotation, then spun down and the supernatant transferred to a fresh tube. The beads were washed 3 times with 100 μl of 6×SSC, resuspended in 50 μl H2O, and heated to 72° C. for 5 min to elute RNA off the beads. The elution step was repeated once and the elutions pooled. All steps of the hybridization (RNA input, hybridization supernatant, three washes, and combined elution) were separately purified using the Qiagen RNeasy Plus Mini Kit according to the manufacturers' instructions. Various dilutions of the elutions were used in RT-ddPCR reactions with primers and probes for either ACTB or GAPDH.

Fluidigm C1 experiments. C1 experiments were performed as previously described (Shalek et al., 2014). Briefly, suspensions of 3T3 and HEK cells were stained with calcein violet and calcein orange (Life Technologies) according to the manufacturer's recommendations, diluted down to a concentration of 250,000 cells per mL, and mixed 1:1. This cell mixture was then loaded into two medium C1 cell capture chips from Fluidigm and, after loading, caught cells were visualized and identified using DAPI and TRITC fluorescence. Bright field images were used to identify ports with >1 cell (a total of 12 were identified from the two C1 chips used, out of 192 total). After C1-mediated whole transcriptome amplification, libraries were made using Nextera XT (Illumina), and loaded on a NextSeq 500 at 2.2 μM. Single-read sequencing (60 bp) was performed to mimic the read structure in DropSeq, and the reads aligned as per below.

Read alignment and generation of digital expression data. Raw sequence data was first filtered to remove all read pairs with a barcode base quality of less than 10. The second read (50 or 60 bp) was then trimmed at the 5′ end to remove any TSO adapter sequence, and at the 3′ end to remove polyA tails of length 6 or greater, then aligned to either the mouse (mm10) genome (retina experiments) or a combined mouse (mm10)-human (hg19) mega-reference, using STAR v2.4.0 a with default setting.

Uniquely mapped reads were grouped by cell barcode. To digitally count gene transcripts, a list of UMIs in each gene, within each cell, was assembled, and UMIs within ED=1 were merged together. The total number of unique UMI sequences was counted, and this number was reported as the number of transcripts of that gene for a given cell.

To distinguish cell barcodes arising from STAMPs, rather than those that corresponded to beads never exposed to cell lysate, Applicants ordered the digital expression matrix by the total number of transcripts per cell barcode, and plotted the cumulative fraction of all transcripts in the matrix for each successively smaller cell barcode. Empirically, Applicants' data always displays a “knee,” at a cell barcode number close to the estimate number of STAMPs amplified (FIG. 14B). All cell barcodes larger than this cutoff were used in downstream analysis, while the remaining cell barcodes were discarded.

Cell cycle analysis of HEK and 3T3 cells. Gene sets reflecting five phases of the HeLa cell cycle (G1/S, S, G2/M, M and M/G1) were taken from Whitfield et al. (Whitfield et al., 2002) (Table 3), and refined by examining the correlation between the expression pattern of each gene and the average expression pattern of all genes in the respective gene-set, and excluding genes with a low correlation (R<0.3). This step removed genes that were identified as phase-specific in Hela cells but did not correlate with that phase in Applicants' single cell data. The remaining genes in each refined gene-set were highly correlated (not shown). Applicants then averaged the normalized expression levels (log₂(TPM+1)) of the genes in each gene-set to define the phase-specific scores of each cell. These scores were then subjected to two normalization steps. First, for each phase, the scores were centered and divided by their standard deviation. Second, the normalized scores of each cell were centered and normalized.

To order cells according to their progression along the cell cycle, Applicants first compared the pattern of phase-specific scores, of each cell, to eight potential patterns along the cell cycle: only G1/S is on, both G1/S and S, only S, only G2/M, G2/M and M, only M, only M/G1, M/G1 and G1. Applicants also added a ninth pattern for equal scores of all phases (either all active or all inactive). Each pattern was defined simply as a vector of ones for active programs and zeros for inactive programs. Applicants then classified the cells to the defined patterns based on the maximal correlation of the phase-specific scores to these potential patterns. Importantly, none of the cells were classified to the ninth pattern of equal activity, while multiple cells were classified to each of the other patterns. To further order the cells within each class Applicants sorted the cells based on their relative correlation with the preceding and succeeding patterns, thereby smoothing the transitions between classes (FIG. 10A).

To identify cell cycle-regulated genes Applicants used the cell cycle ordering defined above and a sliding window approach with a window size of 100 cells. Applicants identified the windows with maximal average expression and minimal average expression for each gene and used a two-sample t-test to assign an initial p-value for the difference between maximal and minimal windows. A similar analysis was performed after shuffling the order of cells in order to generate control p-values that can be used to evaluate false-discovery rate (FDR). Specifically, Applicants examined for each potential p-value threshold, how many genes pass that threshold in the cell-cycle ordered and in the randomly-ordered analyses to assign FDR. Genes were defined as being previously known to be cell-cycle regulated if they were included in a cell cycle GO/KEGG/REACTOME gene set, or reported in a recent genome-wide study of gene expression in synchronized replicating cells (Bar-Joseph et al., 2008).

Unsupervised dimensionality reduction and clustering analysis of retina data. P14 mouse retina suspensions were processed through Drop-Seq in seven different replicates on four separate days, and each sequenced separately. Raw digital expression matrices were generated for the seven sequencing runs. The inflection points (number of cells) for each sample replicate were as follows: 6,600, 9,000, 6,120, 7,650, 7,650, 8280, and 4000. The full 49,300 cells were merged together in a single matrix, and first normalized by the number of UMIs by dividing by the total number of UMIs per cell, then multiplied by 10,000. All calculations and data were then performed in log space (i.e. ln(transcripts-per-10,000+1)).

Initial downsampling and identification of highly variable genes. Rod photoreceptors constitute 60-70% of the retinal cell population. Furthermore, they are significantly smaller than other retinal cell types (Carter-Dawson and LaVail, 1979), and as a result yielded significantly fewer genes (and higher levels of noise) in Applicants' single cell data. In Applicants' preliminary computational experiments, performing unsupervised dimensionality reduction on the full dataset resulted in representations that were dominated by noisy variation within the numerous rod subset; this compromised Applicants' ability to resolve the heterogeneity within other cell-types that were comparatively smaller in frequency (e.g. amacrines, microglia). Thus, to increase the power of unsupervised dimensionality reduction techniques for discovering these types Applicants first downsampled the 49,300-cell dataset to extract single-cell libraries where 900 or more genes were detected, resulting in a 13,155-cell “training set”. Applicants reasoned that this “training set” would be enriched for rare cell types that are larger in size at the expense of “noisy” rod cells. The remaining 36,145 cells (henceforth “projection set”) were then directly embedded onto to the low dimensional representation learned from the training set (see below). This enabled us to leverage the full statistical power of Applicants' data to define and annotate cell types.

Applicants first identified the set of genes that was most variable across the training set, after controlling for the relationship between mean expression and variability. Applicants calculated the mean and a dispersion measure (variance/mean) for each gene across all 13,155 single cells, and placed genes into 20 bins based on their average expression. Within each bin, Applicants then z-normalized the dispersion measure of all genes within the bin, in order to identify outlier genes whose expression values were highly variable even when compared to genes with similar average expression. Applicants used a z-score cutoff of 1.7 to identify 384 significantly variable genes, which as expected, consisted of markers for distinct retinal cell types.

Principal Components Analysis. Applicants ran Principal Components Analysis (PCA) on Applicants' training set as previously described (Shalek et al., 2013), using the prcomp function in R, after scaling and centering the data along each gene. Applicants used only the previously identified “highly variable” genes as input to the PCA in order to ensure robust identification of the primary structures in the data.

While the number of principal components returned is equal to the number of profiled cells, only a small fraction of these components explain a statistically significant proportion of the variance, as compared to a null model. Applicants used two approaches to identify statistically significant PCs for further analysis: (1) Applicants performed 10000 independent randomizations of the data such that within each realization, the values along every row (gene) of the scaled expression matrix are randomly permuted. This operation randomizes the pairwise correlations between genes while leaving the expression distribution of every gene unchanged. PCA was performed on each of these 10000 “randomized” datasets. Significant PCs in the un-permuted data were identified as those with larger eigenvalues compared to the highest eigenvalues across the 10000 randomized datasets (p<0.01, Bonferroni corrected). (2) Applicants modified a randomization approach (‘jack straw’) proposed by Chung and Storey (Chung and Storey, 2014) and which Applicants have previously applied to single-cell RNA-seg data (Shalek et al., 2014). Briefly, Applicants performed 1,000 PCAs on the input data, but in each analysis, Applicants randomly ‘scrambled’ 1% of the genes to empirically estimate a null distribution of scores for every gene. Applicants used the joint-null criterion (Leek and Storey, 2011) to identify PCs that had gene scores significantly different from the respective null distributions (p<0.01, Bonferroni corrected). Both (1) and (2) yielded 32 ‘significant’ PCs. Visual inspection confirmed that none of these PCs was primarily driven by mitochondrial, housekeeping, or hemoglobin genes. As expected, markers for distinct retinal cell types were highly represented among the genes with the largest scores (+ve and −ve) along these PCs (Table 5).

t-SNE representation and post-hoc projection of remaining cells. Because canonical markers for different retinal cell types were strongly represented along the significant PCs (FIG. 17 ), Applicants reasoned that the loadings for individual cells in the training set along the principal eigenvectors (also “PC subspace representation”) could be used to separate out distinct cell types in the data. Applicants note that these loadings leverage information from the 384 genes in the PCA, and therefore are more robust to technical noise than single-cell measurements of individual genes. Applicants used these PC loadings as input for t-Distributed Stochastic Neighbor Embedding (tSNE) (van der Maaten and Hinton, 2008), as implemented in the tsne package in R with the “perplexity” parameter set to 30. The t-SNE procedure returns a two-dimensional embedding of single cells. Cells with similar expression signatures of genes within Applicants' variable set, and therefore similar PC loadings, will likely localize near each other in the embedding, and hence distinct cell types should form two-dimensional point clouds across the tSNE map.

Prior to identifying and annotating the clusters, Applicants projected the remaining 36,145 cells (the projection set) onto the tSNE map of the training set by the following procedure:

-   -   (1) Applicants projected these cells onto the subspace defined         by the significant PCs identified from the training set.         Briefly, Applicants centered and scaled the 384×36,145         expression matrix corresponding to the projection set,         considering only the highly variable genes; the scaling         parameters of training set were used to center and scale each         row. Applicants then multiplied the transpose of this scaled         expression matrix with the 384×32 gene scores matrix learned         from the training set PCA. This yields a PC “loadings” for the         cells in the projection set along the 32 significant PCs learned         on the training set.     -   (2) Based on its PC loadings, each cell in the projection set         was independently embedded on to the tSNE map of the training         set introduced earlier using a mathematical framework consistent         with the original tSNE algorithm (Shekhar et al., 2014).         Applicants note that while this approach does not discover novel         clusters outside of the ones identified from the training set,         it sharpens the distinctions between different clusters by         leveraging the statistical power of the full dataset. Moreover,         the cells are projected based on their PC signatures, not the         raw gene expression values, which makes Applicants' approach         more robust against technical noise in individual gene         measurements.

See section “Embedding the projection set onto the tSNE map” below for full details.

One potential concern with this “post-hoc projection approach” was the possibility that a cell type that is completely absent from the training set might be spuriously projected into one of the defined clusters. Applicants tested the projection algorithm on a control dataset to explore this possibility, and placed stringent conditions to ensure that only cell types adequately represented within the training set are projected to avoid spurious assignments (see ‘“Out of sample” projection test’). Using this approach, 97% of the cells in the projection set were successfully embedded, resulting in a tSNE map consisting of 48296 out of 49300 sequenced cells (Table 10).

As an additional validation of Applicants' approach, it was noted that the relative frequencies of different cell types identified after clustering the full data (see below) closely matches estimates in the literature (Table 1). With the exception of the rods, all the other cell-types were enriched at a median value of 2.3× in the training set compared to their frequency of the full data. This strongly suggests that Applicants' downsampling approach indeed increases the representation of other cell types at the expense of the rod cells, enabling us to discover PCs that define these cells.

Density clustering to identify cell-types. To automatically identify putative cell types on the tSNE map, Applicants used a density clustering approach implemented in the DBSCAN R package (Ester et al., 1996), setting the reachability distance parameter (eps) to 1.9, and removing clusters less than 50 cells. The majority of the removed cells included singleton cells that were located between the interfaces of bigger clusters. As a result of these steps, Applicants were able to assign 44808 cells (91% of the data) into 49 clusters.

Applicants next examined the 49 total clusters, to ensure that the identified clusters truly represented distinct cellular classifications, as opposed to over-partitioning. Applicants performed a post-hoc test where Applicants searched for differentially expressed genes (McDavid et al., 2013) between every pair of clusters (requiring at least 10 genes, each with an average expression difference greater than 1 natural log value between clusters with a Bonferroni corrected p<0.01). Applicants iteratively merged cluster pairs that did not satisfy this criterion, starting with the two most related pairs (lowest number of differentially expressed genes). This process resulted in 10 merged clusters, leaving 39 remaining.

Applicants then computed average gene expression for each of the 39 remaining clusters, and calculated Euclidean distances between all pairs, using this data as input for complete-linkage hierarchical clustering and dendrogram assembly. Applicants then compared each of the 39 clusters to the remaining cells using a likelihood-ratio test (McDavid et al., 2013) to identify marker genes that were differentially expressed in the cluster.

Embedding the projection set onto the tSNE map. Applicants used the computational approach in Shekhar et al (Shekhar et al., 2014) and Berman et al. (Berman et al., 2014) to project new cells onto an existing tSNE map. First, the expression vector of the cell is reduced to include only the set of highly variable genes, and subsequently centered and scaled along each gene using the mean and standard deviation of the gene expression in the training set. This scaled expression vector z (dimensions 1×384) is multiplied with the scores matrix of the genes S (dimensions 384×32), to obtain its “loadings” along the significant PCs u (dimensions 1×32).

Thus, u′=z′. S

u (dimensions 1×32) denotes the representation of the new cell in the PC subspace identified from the training set. Applicants note a point of consistency here in that performing the above dot product on a scaled expression vector of a cell z taken from the training set recovers its correct subspace representation u, as it ought to be the case.

Given the PC loadings of the cells in the training set {u′} (i=1, 2, . . . N_(train)) and their tSNE coordinates {y^(i)} (i=1, 2, . . . N_(train)), the task now is to find the tSNE coordinates y′ of the new cell based on its loadings vector u′. As in the original tSNE framework (van der Maaten and Hinton, 2008), Applicants “locate” the new cell in the subspace relative to the cells in the training set by computing a set of transition probabilities,

${p\left( u^{\prime} \middle| u^{i} \right)} = \frac{\exp\left( {{{- {d\left( {u^{\prime},u^{i}} \right)}^{2}}/2}\sigma_{u^{\prime}}^{2}} \right)}{\sum_{\{ u^{i}\}}{\exp\left( {{{- {d\left( {u^{\prime},u^{i}} \right)}^{2}}/2}\sigma_{u^{\prime}}^{2}} \right)}}$

Here, d(. , .) represents Euclidean distances, and the bandwidth σ_(u′) is chosen by a simple binary search in order to constrain the Shannon entropy associated with p(u′|u^(i)) to log₂(30), where 30 corresponds to the value of the perplexity parameter used in the tSNE embedding of the training set. Note that σ_(u′) is chosen independently for each cell.

A corresponding set of transition probabilities in the low dimensional embedding are defined based on the Student's t-distribution as,

${q\left( y^{\prime} \middle| y^{i} \right)} = \frac{\left( {1 + {d\left( {y^{\prime},y^{i}} \right)}^{2}} \right)^{- 1}}{\sum_{\{ y^{i}\}}\left( {1 + {d\left( {y^{\prime},y^{i}} \right)}^{2}} \right)^{- 1}}$ where y′ are the coordinates of the new cell that are unknown. Applicants calculate these by minimizing the Kullback-Leibler divergence between p(u′|u^(i)) and q(y′|y^(i)),

$y^{\prime} = {\arg\;\min{\sum\limits_{i}{{p\left( u^{\prime} \middle| u^{i} \right)}\log\frac{p\left( u^{\prime} \middle| u^{i} \right)}{q\left( y^{\prime} \middle| y^{i} \right)}}}}$

This is a non-convex objective function with respect to its arguments, and is minimized using the Nelder-Mead simplex algorithm, as implemented in the Matlab function fminsearch. This procedure can be parallelized across all cells in the projection set.

A few notes on the implementation,

-   -   1. Since this is a post-hoc projection, and p(u′|u^(i)) is only         a relative measure of pairwise similarity in that it is always         constrained to sum to 1, Applicants wanted to avoid the         possibility of new cells being embedded on the tSNE map by         virtue of their high relative similarity to one or two training         cells (“short circuiting”). In other words, Applicants chose to         project only those cells that were drawn from regions of the PC         subspace that were well represented in the training set by at         least a few cells. Thus, Applicants retained a cell u′ for         projection only if p(u′|u^(i))>p_(thres) was true for at least         N_(min) cells in the training set (p_(thres)=5×10⁻³,         N_(min)=10). Applicants calibrated the values for p_(thres) and         N_(min) by testing the projection algorithm on cases where the         projection set was known to be completely different from the         training set to ensure that such cells were largely rejected by         this constraint. (see Section ‘“Out of sample” projection         test’).     -   2. For cells that pass the constraint in pt. 1., the initial         value of the tSNE coordinate y′₀ is set to,

$y_{0}^{\prime} = {\sum\limits_{i}{{p\left( u^{\prime} \middle| u^{i} \right)}y^{i}}}$

-   -    i.e. a weighted average of the tSNE coordinates of the training         set with the weights set to the pairwise similarity in the PC         subspace representation.     -   3. A cell satisfying the condition in 1. is said to be         “successfully projected” to a location y′* when a minimum of the         KL divergence could be found within the maximum number of         iterations. However since the program is non-convex and is         guaranteed to only find local minima, Applicants wanted to         explore if a better minima could be found. Briefly, Applicants         uniformly sampled points from a 25×25 grid centered on y′* to         check for points where the value of the KL-divergence was within         5% of its value at y′* or lower. Whenever this condition was         satisfied (<2%) of the time, Applicants re-ran the optimization         by setting the new point as the initial value.

“Out of sample” projection test. In order to test the post-hoc projection method, Applicants conducted the following computational experiment wherein each of the 39 distinct clusters on the tSNE map was synthetically “removed” from the tSNE map, and then reprojected cell-by-cell on the tSNE map of the remaining clusters using the procedure outlined above. Only cells from the training set were used in these calculations.

Assuming Applicants' cluster distinctions are correct, in each of these 39 experiments, the cluster that is being reprojected represents an “out of sample” cell type. Thus successful assignments of these cells into one of the remaining 38 clusters would be spurious. For each of the 39 clusters that was removed and reprojected, Applicants classified the cells into three groups based on the result of the projection method

-   -   (1) Cells that did not satisfy the condition 1. in the previous         section (i.e. did not have a high relative similarity to at         least N_(min) training cells), and therefore “failed” to         project.     -   (2) Cells that were successfully assigned a tSNE coordinate y′,         but that could not be assigned into any of the existing clusters         according to the condition below.     -   (3) Cells that were successfully assigned a tSNE coordinate y′,         and which were “wrongly assigned” to one of the existing         clusters. A cell was assigned to a cluster whose centroid was         closest to y′ if and only if the distance between y′ and the         centroid was smaller than the cluster radius (the distance of         the farthest point from the centroid).

Encouragingly for all of the 39 “out of sample” projection experiments, only a small fraction of cells were spuriously assigned to one of the clusters, i.e. satisfied (3) above with the parameters p_(thres)=5×10⁻³ and N_(min)=10 (Table 10). This provided confidence that Applicants' post-hoc embedding of the projection set would not spuriously assign distinct cell types into one of the existing clusters.

Downsampling analyses of retina data. To generate the 500-cell and 2000-cell downsampled tSNE plots shown in FIG. 11F, cells were randomly sampled from the high-purity replicate (replicate 7), and used as input for PCA and tSNE. The 500-cell tSNE was clustered using a reachability distance parameter (eps) of 5.5, while the 2000-cell tSNE was clustered using an eps value of 3.0. Unclustered cells were removed. To generate the 9,431-cell downsampled tSNE plot, 10,000 cells were randomly sampled from the full dataset, and the cells expressing transcripts from more than 900 genes were used in principal components analysis and tSNE; the remaining (smaller) cells were projected onto the tSNE embedding, and clustered using an eps value of 2.0, resulting in a plot with 9,431 cells.

Immunohistochemistry. Wild-type C57 mice or Mito-P mice, which express CFP in nGnG amacrine and Type 1 bipolar cells (Kay et al., 2011), were euthanized by intraperitoneal injection of pentobarbital. Eyes were fixed in 4% PFA in PBS on ice for one hour, followed by dissection and post-fixation of retinas for an additional 30 mins, then rinsed with PBS. Retinas were frozen and sectioned at 20 μm in a cryostat. Sections were incubated with primary antibodies (chick anti-GFP [Abcam] or rabbit anti-PPPIR17 [Atlas]) overnight at 4° C., and with secondary antibodies (Invitrogen and Jackson ImmunoResearch) for 2 hrs at room temperature. Sections were then mounted using Fluoromount G (Southern Biotech) and viewed with an Olympus FVB confocal microscope.

Note on bead surface primers and custom sequencing primers. During the course of experiments for this paper, Applicants used two batches of beads that had two slightly different sequences (Barcoded Bead SeqA and Barcoded Bead SeqB, Table 9). Barcoded Bead SeqA was used in the human-mouse experiments, and in replicates 1-3 of the retina experiment.

Replicates 4-7 were performed with Barcoded Bead SeqB. To prime read 1 for Drop-Seq libraries produced using Barcoded Bead SeqA beads, Read1CustSeqA was used; to prime read 2 for Drop-Seq libraries produced using Barcoded Bead SeqB beads, Read1CustSeqB was used. ChemGenes plans to manufacture large-scale numbers of beads harboring the Barcoded Bead SeqB sequence. These beads should be used with Read1CustSeqB.

Additional Notes Regarding Drop-Seq Implementation

Cell and bead concentrations. Applicants' experiments have shown that the cell concentration used in Drop-Seq has a strong, linear relationship to the purity and doublet rates of the resulting libraries (FIGS. 9A, 9B, and 14D). Cell concentration also linearly affects throughput: ˜10,000 single-cell libraries can be processed per hour when cells are used at a final concentration of 100 cells/ul, and ˜1,200 can be processed when cells are used at a final concentration of 12.5 cells/ul. The trade-off between throughput and purity is likely to affect users differently, depending on the specific scientific questions being asked. Currently, for the standard experiments, Applicants use a final concentration of 50 cells/ul, tolerating a small percentage of doubles and cell contaminants, to be able to easily and reliably process 10,000 cells over the course of a couple of hours. As recommended above, Applicants currently favor loading beads at a concentration of 120/ul (final concentration in droplets=60/ul), which empirically yields a <5% bead doublet rate.

Drop-Seq start-up costs. The main pieces of equipment required to implement Drop-Seq are three syringe pumps (KD Legato 100 pumps, list price ˜$2,000 each) a standard inverted microscope (Motic AE31, list price ˜$1,900), and a magnetic stirrer (V&P scientific, #710D2, list price ˜$1,200). A fast camera (used to monitor droplet generation in real time) is not necessary for the great majority of users (droplet quality can easily be monitored by simply placing 3 ul of droplets in a Fuchs-Rosenthal hemocytometer with 17 ul of droplet generation oil to dilute the droplets into a single plane of focus).

Example 4: Tables for Examples 2 and 3

TABLE 1 Ascertainment of cell types and frequencies in the mouse retina by Drop-Seq. The sizes of the 39 annotated cell clusters produced from Drop-Seq were used to estimate their fractions of the total cell population. These data were compared with those obtained by microscopy techniques (Jeon et al., 1998). Percentage of retina Percentage of (Jeon et al., cell population Cell class 1998) (%) in Drop-Seq (%) Rod photoreceptors 79.9 65.6 Cone photoreceptors 2.1 4.2 Muller glia 2.8 3.6 Retinal ganglion cells 0.5 1.0 Horizontal cells 0.5 0.6 Amacrine cells 7.0 9.9 Bipolar cells 7.3 14 Microglia — 0.2 Retinal endothelial — 0.6 cells Astrocytes 0.1

TABLE 1 Edit distance relationships among UMIs. For the data in FIG. 3G, the sequences of the UMIs for each ERCC gene detected in each cell barcode were collapsed at an edit distance of 1, including only substitutions (left column) or with both substitutions and insertions/deletions (right column). A control UMI set was prepared for each gene, using an equal number of UMIs sampled randomly across all genes/cells. The percent of the original UMIs that were collapsed for each condition are reported in the table. UMI % Reduction in UMI counts Sampling Substitution-only collapse Indel and substitution collapse Within a 68.2% 76.1% gene Across genes 19.1% 45.7%

TABLE 2 Top 100 genes represented in each of the first 5 principal components calculated from the human (HEK) single-cell expression data. PC1 PC2 PC3 PC4 PC5 OPTN CENPE MT-RNR2 CCNB1 PAPOLA H1F0 CENPF DDX21 PSRC1 DTL CREBRF KIF14 GPATCH4 CDC20 TAF7 RHOU TPX2 WDR43 AURKA RTN4 NEAT1 TOP2A LYAR PLK1 TOP1 PRSS23 AURKA FAM211A CKS2 CDCA7 RIT1 DLGAP5 MYBBP1A KIF20A E2F3 CDKN1A DEPDC1 GNL3 HMMR HSP90AA1 MAF SGOL2 NCL PTTG1 TUG1 MALAT1 PRC1 RSL1D1 CENPA HSPH1 CCNE2 CCNB1 RPF2 CDCA3 DYNLL1 DDIT3 ASPM MYC BUB1 ZNF367 MAP1A ARL6IP1 DKC1 CCNB2 MORF4L2 MTRNR2L12 HMMR LARP1 TUBA1C AASDHPPT PPP1R15A PLK1 NOP58 PIF1 HNRNPH3 ATXN1 MALAT1 CD3EAP DEPDC1 HSP90AB1 DGCR8 MKI67 SLC6A15 SGOL2 HIST1H2AC MT-RNR2 CDCA3 PA2G4 KIF2C KTN1 TES TTK NOP14 AURKB ZRANB2 FNIP1 CDC20 SNHG3 TIMM10 HIST1H2BD SAT1 SMC4 DNAJC2 TPX2 ZNF738 ZNF608 BUB1 HEATR1 TUBB4B PSMD10 WDR76 CKS2 NOP16 CENPE PSMD14 NFIB TACC3 NOP56 CDCA8 SET ERO1LB CKAP2 SET UBE2C SSB MXD1 GTSE1 PUS7 G2E3 EIF4G2 TSPYL4 CKAP5 WDR3 GOT1 PIGW ARID4A ANLN RRP15 RNF26 HNRNPR HOXA3 G2E3 MTRNR2L12 FAM64A FUBP1 DDAH2 NCAPG NOLC1 GAS2L3 SNHG3 CLU KIF18A QTRTD1 NDC80 ZC3H15 FAM46A NDC80 LTV1 TMEM115 PAIP2 ARID5B HMGB2 MRTO4 XRCC4 DHX29 IFI27L2 CDCA8 SCD FAM83D HSP90B1 SCN9A PIF1 NOB1 NAMPTL ATP6V1G1 KCTD7 UBE2C SLC16A1 MPV17L2 HNRNPH2 TTLL7 NUF2 POLR3G KPNA2 GOLM1 PCDH17 KIF20A KCTD12 ARL6IP1 CMTM6 PLAT KPNA2 SLC1A3 DHRS7B HNRNPU NAB1 KIF11 MTRNR2L8 PRC1 CAP1 CAPRIN2 KIF23 PAK1IP1 CDKN3 STIP1 LYPD1 KIF4A MT-ND5 HSPA1B JAK1 TMSB4X SFPQ NOL8 TACC3 QKI N4BP2 PSRC1 MT-ND2 BUB1B PFDN4 TM7SF2 BUB1B DHX37 INCENP MIS18A TMEM107 KIF20B UTP14A DTWD2 MSH6 ZNF226 KDM5B DPH2 SAPCD2 PPP1CB PHTF1 BIRC5 MTRNR2L1 CCDC86 C11orf58 MTRNR2L8 HP1BP3 NPM1 KRT10 ZNF280B MTRNR2L3 CASC5 NOC3L TRMT61B DNAJA1 DLG3 BRD8 FASN DYNLL1 EID1 TMSB15A PRRC2C SERBP1 DEPDC1B FAM200B UHRF1 CENPA TSR1 MGARP RDX GATA6 NUSAP1 RIOK1 EIF1 VBP1 NOVA1 DBF4 MT-RNR1 PPP1R11 ANP32E C22orf46 CALM2 RRS1 CUTC SKIL RFX7 INCENP NAA15 TTK PTTG1 ZNF280B ECT2 WDR4 PEX3 CSNK1A1L GKAP1 EIF4G3 TAF1D MRPL12 RAB7A CYP1B1 KIF5B UTP20 CDC25B CTNNB1 ZNF107 C6orf62 TNPO2 DNAJC17 CHMP5 LRRCC1 NIPBL CDK6 PPM1B PRPF40A ZNF200 CEP350 ST6GALNAC2 HN1 MRFAP1 DTL PRR11 NAA25 CALM2 INA OTUD7B CKAP2L FAM216A BRI3 ARCN1 ULK1 CCDC18 TCOF1 SAP30 NUP37 HIST1H2BJ CEP70 C10orf2 PSMF1 ENAH MED13L RBBP6 HRK ECT2 STK32C WEE1 ARID4B RRP1B SPAG5 SNRPB2 RAB9B KIF2C NOP2 MED30 DYNC1I2 SIPA1L2 SGOL1 BCAT1 TNIP2 CFL2 FADS2 CCNB2 AMD1 DUSP14 BTF3 ZDBF2 ACIN1 MIR17HG TMEM99 TIPIN KIF1A CDC27 POLR1A RAB28 ARV1 ATF3 U2SURP MDN1 BIRC5 NACA GADD45A ARHGAP11A RRP12 DOHH CHMP2B NEXN CCNA2 PWP1 MAD2L1 ILF2 PPP1R9A CDKN1B RCC1 BOLA1 RPL5 BNIP3L TRA2A GRB14 C14orf119 SLTM C4orf21 RSF1 C8orf33 DCPS NAP1L3 NPHP3 DR1 MTHFD1L PDIA5 PIK3R3 TRPS1 TUBB4B AKAP1 SART1 ADAR COLEC12 BOD1L1 POU3F2 MRPS2 HNRNPK ZFHX3 NCAPD2 TTLL12 MIOS CAPRIN1 SNAPC5 KIF4B FAM208B CSTB METAP2 REV3L CDCA2 EIF5B RANGAP1 CSNK1A1 REST USP9X CEBPZ TFCP2 NCKAP1 ANKRD12 RANGAP1 STARD7 MAP7D1 CBX1 YPEL5 SON CDV3 CETN3 CDV3 UBE2H CCAR1 PNO1 GTPBP6 KRR1 SERPINB1 TNRC6B ABCE1 RACGAP1 KPNA4 ZNF367 GOLGA4 JSRP1 CKAP5 HMMR SMARCA1 SRRM2 PAWR SPR TMEM167A BAZ2B LBR TIMM44 SAMD8 MMADHC SESN3 PTTG1 TWISTNB MRPL3 ISCU C1orf63 NEK2 TFRC FBXO38 NACA2 HOXA-AS4 AURKB MT-ND4 ZNRD1 FXR1 ZFP90 RBMX IPO7 CENPF HSPA14 NFAT5 HEXIM1 MTPAP C8orf76 PSMD4 ZNF711 CCDC88A HSPD1 PES1 MARCH5

TABLE 3 Genes used for each phase of the cell cycle for the analysis in FIG. 4. G1/S S G2/M M M/G1 ACD ABCC5 ANLN AHI1 AGFG1 ACYP1 ABHD10 AP3D1 AKIRIN2 AGPAT3 ADAMTS1 ANKRD18A ARHGAP19 ANKRD40 AKAP13 ANKRD10 ASF1B ARL4A ANLN AMD1 APEX2 ATAD2 ARMC1 ANP32B ANP32E ARGLU1 BBS2 ASXL1 ANP32E ANTXR1 ATAD2 BIVM ATL2 ARHGAP19 BAG3 BARD1 BLM AURKB ARL6IP1 BTBD3 BRD7 BMI1 BCLAF1 ASXL1 CBX3 C1orf63 BRCA1 BORA ATF7IP CDC42 C7orf41 BRIP1 BRD8 AURKA CDK7 C14orf142 C5orf42 BUB3 BIRC2 CDKN3 CAPN7 C11orf82 C2orf69 BIRC5 CEP70 CASP2 CALD1 C14orf80 BUB1 CNIH4 CASP8AP2 CALM2 CASP3 CADM1 CTR9 CCNE1 CASP2 CBX5 CCDC88A CWC15 CCNE2 CCDC14 CCDC107 CCDC90B DCP1A CDC6 CCDC84 CCNA2 CCNA2 DCTN6 CDC25A CCDC150 CCNF CCNB2 DEXI CDCA7 CDC7 CDC16 CDC20 DKC1 CDCA7L CDC45 CDC25C CDC25B DNAJB6 CEP57 CDCA5 CDCA2 CDC27 DSP CHAF1A CDKN2AIP CDCA3 CDC42EP1 DYNLL1 CHAF1B CENPM CDCA8 CDCA3 EIF4E CLSPN CENPQ CDK1 CENPA ELP3 CREBZF CERS6 CDKN1B CENPE FAM60A CTSD CHML CDKN2C CENPF FAM189B DIS3 COQ9 CDR2 CEP55 FOPNL DNAJC3 CPNE8 CENPL CFLAR FOXK2 DONSON CREBZF CEP350 CIT FXR1 DSCC1 CRLS1 CFD CKAP2 G3BP1 DTL DCAF16 CFLAR CKAP5 GATA2 E2F1 DEPDC7 CHEK2 CKS1B GNB1 EIF2A DHFR CKAP2 CKS2 GRPEL1 ESD DNA2 CKAP2L CNOT10 GSPT1 FAM105B DNAJB4 CYTH2 CNTROB GTF3C4 FAM122A DONSON DCAF7 CTCF HIF1A FLAD1 DSCC1 DHX8 CTNNA1 HMG20B GINS2 DYNC1LI2 DNAJB1 CTNND1 HMGCR GINS3 E2F8 ENTPD5 DEPDC1 HSD17B11 GMNN EIF4EBP2 ESPL1 DEPDC1B HSPA8 HELLS ENOSF1 FADD DIAPH3 ILF2 HOXB4 ESCO2 FAM83D DLGAP5 JMJD1C HRAS EXO1 FAN1 DNAJA1 KDM5B HSF2 EZH2 FANCD2 DNAJB1 KIAA0586 INSR FAM178A G2E3 DR1 KIF5B INTS8 FANCA GABPB1 DZIP3 KPNB1 IVNS1ABP FANCI GAS1 E2F5 KRAS KIAA1147 FEN1 GAS2L3 ECT2 LARP1 KIAA1586 GCLM H2AFX FAM64A LARP7 LNPEP GOLGA8A HAUS8 FOXM1 LRIF1 LUC7L3 GOLGA8B HINT3 FYN LYAR MCM2 H1F0 HIPK2 G2E3 MORF4L2 MCM4 HELLS HJURP GADD45A MRPL19 MCM5 HIST1H2AC HMGB2 GAS2L3 MRPS2 MCM6 HIST1H4C HN1 GOT1 MRPS18B MDM1 INTS7 HP1BP3 GRK6 MSL1 MED31 KAT2A HRSP12 GTSE1 MTPN MRI1 KAT2B IFNAR1 HCFC1 NCOA3 MSH2 KDELC1 IQGAP3 HMG20B NFIA NASP KIAA1598 KATNA1 HMGB3 NFIC NEAT1 LMO4 KCTD9 HMMR NUCKS1 NKTR LYRM7 KDM4A HN1 NUFIP2 NPAT MAN1A2 KIAA1524 HP1BP3 NUP37 NUP43 MAP3K2 KIF5B HPS4 ODF2 ORC1 MASTL KIF11 HS2ST1 OPN3 OSBPL6 MBD4 KIF20B HSPA8 PAK1IP1 PANK2 MCM8 KIF22 HSPA13 PBK PCDH7 MLF1IP KIF23 INADL PCF11 PCNA MYCBP2 KIFC1 KIF2C PLIN3 PLCXD1 NAB1 KLF6 KIF5B PPP2CA PMS1 NEAT1 KPNA2 KIF14 PPP2R2A PNN NFE2L2 LBR KIF20B PPP6R3 POLD3 NRD1 LIX1L KLF9 PRC1 RAB23 NSUN3 LMNB1 LBR PSEN1 RECQL4 NT5DC1 MAD2L1 LMNA PTMS RMI2 NUP160 MALAT1 MCM4 PTTG1 RNF113A OGT MELK MDC1 RAD21 RNPC3 ORC3 MGAT2 MIS18BP1 RAN SEC62 OSGIN2 MID1 MKI67 RHEB SKP2 PHIP MIS18BP1 MLLT4 RPL13A SLBP PHTF1 MND1 MZT1 SLC39A10 SLC25A36 PHTF2 NCAPD3 NCAPD2 SNUPN SNHG10 PKMYT1 NCAPH NCOA5 SRSF3 SRSF7 POLA1 NCOA5 NEK2 STAG1 SSR3 PRIM1 NDC80 NUF2 SYNCRIP TAF15 PTAR1 NEIL3 NUP35 TAF9 TIPIN RAD18 NFIC NUP98 TCERG1 TOPBP1 RAD51 NIPBL NUSAP1 TLE3 TRA2A RAD51AP1 NMB ODF2 TMEM138 TTC14 RBBP8 NR3C1 ORAOV1 TOB2 UBR7 REEP1 NUCKS1 PBK TOP1 UHRF1 RFC2 NUMA1 PCF11 TROAP UNG RHOBTB3 NUSAP1 PLK1 TSC22D1 USP53 RMI1 PIF1 POC1A TULP4 VPS72 RPA2 PKNOX1 POM121 UBE2D3 WDR76 RRM1 POLQ PPP1R10 VANGL1 ZMYND19 RRM2 PPP1R2 PRPSAP1 VCL ZNF367 RSRC2 PSMD11 PRR11 WIPF2 ZRANB2 SAP30BP PSRC1 PSMG3 WWC1 SLC38A2 RANGAP1 PTP4A1 YY1 SP1 RCCD1 PTPN9 ZBTB7A SRSF5 RDH11 PWP1 ZCCHC10 SVIP RNF141 QRICH1 ZNF24 TOP2A SAP30 RAD51C ZNF281 TTC31 SKA3 RANGAP1 ZNF593 TTLL7 SMC4 RBM8A TYMS STAT1 RCAN1 UBE2T STIL RERE UBL3 STK17B RNF126 USP1 SUCLG2 RNF141 ZBED5 TFAP2A RNPS1 ZWINT TIMP1 RRP1 TMEM99 SEPHS1 TMPO SETD8 TNPO2 SFPQ TOP2A SGOL2 TRAIP SHCBP1 TRIM59 SMARCB1 TRMT2A SMARCD1 TTF2 SPAG5 TUBA1A SPTBN1 TUBB SRF TUBB2A SRSF3 TUBB4B SS18 TUBD1 SUV420H1 UACA TACC3 UBE2C THRAP3 VPS25 TLE3 VTA1 TMEM138 WSB1 TNPO1 ZNF587 TOMM34 ZNHIT2 TPX2 TRIP13 TSG101 TSN TTK TUBB4B TXNDC9 TXNRD1 UBE2D3 USP13 USP16 VANGL1 WIBG WSB1 YWHAH ZC3HC1 ZFX ZMYM1 ZNF207

TABLE 4 List of cell cycle regulated genes identified from the analysis of 589 HEK and 412 3T3 cells. Intersection human novel gene cluster mouse gene All genes genes annotation CCNE2 1 Shmt1 CDC6 1 Zmym1 ACTB ACTB CLSPN 1 Meaf6 AKIRIN2 ARHGAP11A DTL 1 Usp37 ANLN ARL6IP6 MCM3 1 Msh6 ANP32E ARPC2 MCM5 1 Rbbp4 ARHGAP11A ATF4 TF MCM6 1 Bri3bp ARL6IP1 CCAR1 MSH6 1 Rrp8 ARL6IP6 CCDC18 PCNA 1 Mb21d1 ARPC2 CDCA4 CC UNG 1 Wdhd1 ASF1B DNAJC9 ADAMTS1 1 Mcm5 ASPM DNMT1 ARL6IP6 1 Smarca5 ATAD2 E2F7 TF/CC ATAD2 1 Slc1a5 ATF4 FTH1 BLM 1 Nap1l4 AURKA GOLGA2 C4orf21 1 Nolc1 AURKB GPSM2 CASP8AP2 1 D10Wsu102e BIRC5 H3F3B CC CCNE1 1 Ckap4 BLM HIST1H1E CC CDCA7 1 Timeless BORA MBNL1 CHAF1A 1 Zfp367 BRD8 MCMBP CC CHAF1B 1 Zmynd19 BRIP1 MRPL17 E2F1 1 Cdc25a BUB1 NCAPG CC E2F8 1 Atp2b1 BUB1B NDUFA1 FEN1 1 Smarcc1 BUB3 NXT1 GINS2 1 Ccnd2 CALM2 OSBPL8 1 Lbh CASC5 OTUB1 HIST1H2BK MCM2 1 Maff CASP8AP2 PARPBP CC MCM7 1 Casp3 CBX5 PRRC2C MCM10 1 Tnfaip8 CCAR1 RPL26 MCMBP 1 Amotl1 CCDC18 SNHG3 MMS22L 1 Rfc1 CCNA2 SRP9 PKMYT1 1 Cdc42ep3 CCNB1 TCF19 TF PRIM1 1 Gpr180 CCNB2 TK1 RAD51 1 Oaf CCNE1 TUBA1C RFC4 1 Gins3 CCNE2 UBC SLBP 1 Cdc7 CCNF WDHD1 SNHG3 1 Cactin CDC6 ZFHX4 TF TIPIN 1 Eps8 CDC20 TK1 1 Slk CDC27 TMEM97 1 Smc3 CDC45 UHRF1 1 Alad CDCA2 WDR76 1 Nasp CDCA3 XRCC2 1 Smc5 CDCA4 ZMYND19 1 Fen1 CDCA7 ZNF367 1 Ctnnal1 CDCA8 CDC45 1 Enkd1 CDK1 DNAJC9 1 Tjp2 CDK5RAP2 DSCC1 1 Nup43 CDKN1B DUT 1 Dek CDKN2C EXO1 1 Slbp CENPA FBXO5 1 Ung CENPE H1F0 1 Paics CENPF HELLS 1 Gins2 CEP55 HIST1H4C 1 Umps CHAF1A HSPB11 1 Pdlim1 CHAF1B IRS4 1 Gart CKAP2 KIAA0101 1 Whsc1 CKAP2L MCM4 1 Baz1b CKAP5 MLF1IP 1 Efnb2 CKS1B MSH2 1 Pola2 CLSPN POLD3 1 Ivns1abp CTCF PSMC3IP 1 Dnaaf2 DBF4 RAD51AP1 1 Trmt2a DLGAP5 RRM2 1 E2f1 DNAJC9 TCF19 1 Chaf1b DNMT1 TYMS 1 Syngr2 DSCC1 UBE2T 1 Mcmbp DTL ACAA1 1 Cdt1 E2F1 ACYP1 1 Pold3 E2F7 ALDOA 1 Ubr7 E2F8 ARID3A 1 Grsf1 ECT2 ARPC2 1 Dck ERCC6L ARPC5 1 Atad5 ESPL1 ASF1B 1 Casp8ap2 EXO1 ASRGL1 1 Orc2 FAM64A ATP5E 1 Siva1 FAM83D ATP6V1D 1 Cdca7 FBXO5 ATP6V1F 1 Rif1 FEN1 ATP6V0E2 1 Ptrh2 FOXM1 B2M 1 Arl6ip6 FTH1 BRIP1 1 Rnf168 G2E3 C1orf21 1 Tfrc GAS2L3 C3orf14 1 Fancl GINS2 C4orf48 2 Clspn GMNN C5orf22 2 Lig1 GOLGA2 C19orf53 2 Gmnn GPSM2 C21orf58 2 Dtl GTSE1 CALM1 2 Uhrf1 H1F0 CAMTA1 2 Ccne1 H3F3B CARHSP1 2 Fam111a HAT1 CCDC51 2 Tcf19 HELLS CDCA4 2 Dnmt1 HEXIM1 CLTB 2 Msh2 HIST1H1E COX6B1 2 Orc6 HJURP COX7C 2 Mcm6 HMGB2 COX8A 2 Pcna-ps2 HMMR COX17 2 Mcm2 HN1 DDX46 2 Hells HP1BP3 DGCR8 2 Haus6 INCENP DMC1 2 Ccne2 KDM5B DNMT1 2 Ppat KIF2C DONSON 2 Dscc1 KIF11 DTYMK 2 Cdc6 KIF14 E2F7 2 Rpa2 KIF15 ERCC6L 2 Atad2 KIF18A FADS1 2 Mcm3 KIF20A FAM178A 2 Pcna KIF20B FANCA 2 Mcm7 KIF23 FAU 2 Chaf1a KIFC1 FTH1 2 Hat1 LIG1 FTL 2 Rrm2 LUC7L3 GAPDH 2 Slfn9 MALAT1 GGCT 2 Rfc3 MBNL1 GMNN 2 Mcm4 MCM2 H2AFZ 3 Ldlr MCM3 HAUS1 3 Amotl2 MCM4 HAUS5 3 Topbp1 MCM5 HOMEZ 3 Ncapd3 MCM6 LAGE3 3 Haus8 MCM7 LIG1 3 Rbl1 MCM10 MED31 3 Rrm1 MCMBP MGST3 3 Elovl5 MED31 MRPL17 3 Dhfr MELK MSANTD3 3 Usp1 MIS18BP1 MYBL2 3 Ncapg2 MKI67 MYL6 3 Asf1b MLF1IP NASP 3 Dcaf15 MRPL17 NDUFA1 3 Tssc4 MSH2 NDUFB1 3 Hjurp MSH6 NDUFB2 3 Hist1h2ak NASP NDUFS5 3 Nup155 NCAPD2 NPAT 3 Skp2 NCAPG NPC2 3 Tdp2 NCAPH NXT1 3 Cbx5 NDC80 OPTN 3 Hspa14 NDUFA1 ORC6 3 Mcm10 NEK2 PGK1 3 Prim1 NUF2 PHTF1 3 Exo1 NUSAP1 PIGX 3 Apbb1ip NXT1 PLSCR1 3 Eri1 ODF2 POLA1 3 Smchd1 ORC6 POLR2H 3 Dnajc9 OSBPL8 POU4F1 3 Akap11 OTUB1 PPDPF 3 Mlf1ip PARPBP RABIF 3 Tyms PCNA RFC2 3 Nfx1 PCNT RNASEH2A 3 E2f7 PKNOX1 RNASEH2C 3 Ubap2 PLK1 RPA3 3 Chtf18 POLA1 RPS5 3 Stub1 POLD3 RRM1 3 Esco2 PPP2R5C S100A10 3 Ezh2 PRC1 SEMA3C 3 Pold1 PRIM1 SERF2 3 Apbb2 PRR11 SHFM1 3 E2f8 PRRC2C SLC25A4 3 Cyp51 PSRC1 SLC25A5 3 Rad54l PTTG1 SNHG1 3 Nxt1 RACGAP1 SNHG9 3 Pola1 RAD51 SNRPD2 3 Rpa3 RAD51AP1 SNX10 3 Fbxo5 RANGAP1 SRP9 3 Il1rl1 RBBP6 SS18L2 3 Fhl2 RFC2 SSR4 3 Mis18a RFC4 STMN1 3 Tex30 RPA3 SVIP 3 Idh2 RPL26 TCEB1 3 Mybl1 RRM1 TIMP1 3 Prkca RRM2 TM7SF2 3 Rer1 SGOL1 TMSB10 3 Blm SGOL2 TOPBP1 3 Rpa1 SKA2 TPM4 3 Pole SLBP TTLL7 3 Rfc2 SMC4 TUBA1A 3 Mtbp SNHG3 UBA52 3 Nup107 SPAG5 UBR7 3 Sqle SPC25 USMG5 3 Cenph SRP9 USP1 3 Plk4 TACC3 WDHD1 3 Apitd1 TCF19 YBEY 3 Lrr1 TIPIN ZNF260 3 Haus3 TK1 ZNF428 3 Slc25a1 TMPO ZNF711 3 Acat2 TOP2A ZNF720 3 Sc4mol TOPBP1 ACTB 3 Smc6 TPX2 AIG1 3 Cdca5 TRIM59 ANKRD36C 3 Tk1 TTK ANXA5 3 Thbs1 TUBA1C ARL13B 3 Cdc45 TUBB4B BAD 3 Cyr61 TYMS BUB3 3 Brca1 UACA C2orf68 3 Lphn2 UBC C19orf43 3 Rad51 UBE2C CBX5 3 Rad51ap1 UBE2T CCDC14 3 Rbmx2 UBR7 CCNL2 3 Nup85 UHRF1 CDADC1 3 Pradc1 UNG CDK1 3 Tipin USP1 CDKN2C 3 Rad18 WDHD1 CIRBP 3 Ankrd1 ZFHX4 CREB5 3 Fignl1 ZMYM1 DBF4B 3 Tanc2 ZMYND19 DDX17 3 Rfc4 DPP9 3 Brip1 DUSP3 3 Etaa1 ELF1 3 Slc7a1 FAM76A 3 Ank3 FAM126A 4 Cdca8 FAM192A 4 Ncapg FANCD2 4 Nuf2 FKBP2 4 Gas2l3 FOXC1 4 Ndc80 FOXM1 4 Pbk GATAD2B 4 Cdkn1b GNPTAB 4 Cdkn2c GOLGA8B 4 G2e3 GPX4 4 Smc2 GTPBP3 4 Tuba1c HIST1H1C 4 Racgap1 HIST1H1E 4 Kif11 HIST2H2AC 4 Incenp HJURP 4 Cep55 HOXA3 4 Dbf4 HOXA10 4 Kif2c HOXB7 4 Fam83d IGF2BP2 4 Ccna2 ING3 4 Prc1 IQGAP3 4 Hmgb2 JUN 4 Aurkb KIAA1524 4 Top2a KIFC1 4 Kif22 LARP7 4 Shcbp1 LRRC49 4 Ect2 MAF 4 Mis18bp1 MED21 4 Spc25 MELK 4 Kif4 N4BP2L2 4 Ccnf NMT2 4 Cenpl NT5C 4 Sgol1 OSBPL3 4 Sgol2 OTUB1 4 Casc5 PERP 4 Mki67 RAB5B 4 Fam64a RBM23 4 Kif20b RBMS1 4 H1f0 ROCK1 4 Smc4 SCP2 4 Kif15 SKA2 4 Prr11 SP3 4 Cdk1 SRSF5 5 Flii TFAP2A 5 Adprhl2 THG1L 5 Col6a1 TIMM17B 5 Ubc TMPO 5 Mcph1 TROAP 5 Col16a1 TSC22D3 5 Cenpn TSIX 5 Trip13 TUBB 5 Mrpl17 TUBGCP3 5 Parva UBA5 5 Myadm UBC 5 Ercc6l XIST 5 Arhgef40 XXYLT1 5 Pdgfrb YWHAB 5 Cd81 ZNF503 5 Ska1 ZNF503- 5 Hist1h1e AS2 ZNF703 5 Ccdc53 ZWINT 5 Espl1 AASDH 5 Aaas AKIRIN2 5 Sp1 ANKRD11 5 Mad2l1 APC 5 Rsu1 ARHGAP11A 5 Cryab ARID2 5 Egln2 ASH1L 5 Tmpo ATF4 5 Mastl ATL2 5 Ephx1 BEX1 5 Tpgs2 BOD1L1 5 Lclat1 BORA 5 Rhno1 BTAF1 5 Foxm1 C6orf62 5 Atf4 C10orf118 5 BC003965 CARD8 5 Osbpl8 CASC5 5 Lmnb1 CCDC18 5 Fez2 CCDC88A 5 Ndufv1 CCNA2 5 Osbpl9 CCNB2 5 Otub1 CCNF 5 Atxn10 CDC27 5 Gtse1 CDCA2 5 Fam173a CDKN1B 5 Gemin6 CENPA 5 Bgn CENPI 5 Rfc5 CEP44 5 Malat1 CEP350 5 Fer CKAP2 5 Ncaph2 CKAP2L 5 Meg3 CKS1B 5 Cdca2 CLCN3 5 Stil COASY 5 Pcnt CSNK1G3 5 Tubb5 CTCF 5 Mdc1 DCP1A 5 Cuta DEPDC1B 5 Tuba1b DIAPH2 5 Cst3 DR1 5 Slc35f5 DSC3 5 Ttk DST 5 Tsen2 EIF1B 5 Raf1 EIF4G3 5 Urod ESPL1 5 Ttf2 FAM64A 5 Srgap2 FAM83D 5 Ndufa1 GAS2L3 5 Ubb GOLGA4 5 Cntln GPSM2 5 Ctcf GTPBP6 5 Fra10ac1 HMGB2 5 Pmp22 HN1 5 Thsd7a HP1BP3 5 Angptl2 ICT1 5 Ube2t INO80D 5 Pknox1 ITSN2 5 Cxcl12 KDM5B 5 Vamp5 KIAA0586 5 Ercc5 KIF2C 5 Kif18a KIF4B 5 Ebag9 KIF5B 5 Sap30 KIF15 5 Ska3 MALAT1 5 Ccdc34 MAP9 5 Atp6v1g1 MSX2 5 Fbln2 MT-ND5 5 Cenpq MT-RNR1 5 Adat2 MT-RNR2 5 Dlk1 NCAPD2 5 Lsm3 NCOA2 5 Xiap NEK2 5 Hirip3 NUSAP1 5 Stag2 OSBPL8 5 Skiv2l PBRM1 5 Cenpc1 PCLO 5 Hcfc1r1 PDZD8 5 Cdk5rap2 PHACTR4 5 Stx4a PHF20L1 5 Gen1 PPP1R12A 5 Fam3c PRR11 5 Uaca PTBP3 5 Chrac1 PTPN1 5 Pcif1 RACGAP1 5 Ing1 RANGAP1 5 Add1 RC3H1 5 Gabarap RICTOR 5 Rnf24 RUFY1 5 Zrsr2 SAFB 5 Tbk1 SERTAD2 5 Lsm2 SGOL1 5 Dbnl SMC4 5 Smoc2 SPAG5 5 Puf60 SPG11 5 Ppp1r35 SRRM2 5 Bub3 TAF3 5 Melk THUMPD1 5 Kifc1 TJP1 5 Dock1 TLE3 5 Gabpb1 TRIO 5 Zwilch TUBA1C 5 Mbnl1 TUBB4B 5 Grn UACA 5 Med31 UBE2D1 5 Ncaph UBLCP1 5 Ifit2 USP9X 5 Id2 VPS13A 5 Cdca4 WAC 5 Ddx49 WDR36 5 Cope WDR53 5 Gsg2 YTHDC1 5 Sass6 ZC3H4 5 Arf2 ZCCHC11 5 Nfu1 ZFR 5 Id3 ZIC5 5 Apip ZMAT2 5 H3f3b ZMYM1 5 Cat ZMYND8 5 Trim59 ZNF280D 5 Lpp ZNF281 5 Dcaf7 ZNF638 5 Rasl11a ZNF652 5 Rtkn2 ZYG11B 5 Ska2 ANLN 5 Bicc1 ARL6IP1 5 Golga2 ASPM 5 Col1a1 AURKA 6 Anln AURKB 6 Kif20a BIRC5 6 Cenpf BRD8 6 Ckap2 BUB1 6 Cenpa BUB1B 6 Bub1 CCNB1 6 Hmmr CDC20 6 Ckap2l CDCA3 6 Aurka CDCA8 6 Pttg1 CENPE 6 Plk1 CENPF 6 Cenpe CKAP5 6 Tacc3 CKS2 6 Tpx2 DBF4 6 Tubb4b DEPDC1 6 Cdc20 DLGAP5 6 Aspm ECT2 6 Ccnb1 G2E3 6 Ckap5 GTSE1 6 Ube2c HMMR 6 Arhgap11a INCENP 6 Birc5 KIF11 6 Kif23 KIF14 6 Nusap1 KIF18A 7 Serpinb8 KIF20A 7 Gm10184 KIF20B 7 Gas5 KIF23 7 Dnm3os KPNA2 7 Chchd7 MKI67 7 Cstb NCAPG 7 Smtn NDC80 7 Fam172a NUF2 7 Cdkn3 PIF1 7 Dlgap5 PLK1 7 Mgea5 PRC1 7 Opa3 PSRC1 7 Tax1bp1 SGOL2 7 Parpbp TACC3 7 Nup37 TOP2A 7 Gas1 TPX2 7 Grem2 TTK 7 Uhrf1bp1l UBE2C 7 Ccnb2 ABCC5 7 Brd8 ABI1 7 Cdc25c ACIN1 7 Nek2 ANP32E 7 Cmas ARFGEF2 7 Mrps16 ARHGAP5 7 Hyls1 ARHGAP12 7 Stk11 ARHGAP19 7 Diap3 ARIH1 7 Bora ATF7IP 7 Cit BPGM 7 Rangap1 C10orf32 7 Tm7sf3 C11orf54 7 Arl2bp CALM2 7 Elp3 CAMLG 7 Map2k2 CCAR1 7 Specc1l CCNJ 7 H2afx CDK5RAP2 7 Smarcb1 CEP70 7 Rad23a COMMD2 7 Fzr1 CREBRF 7 Rfk CTNND1 7 Bax CUL5 7 Cdkn2d DCP2 7 Rhoq DDX21 7 Ccdc77 DESI2 7 Tgif1 DHX36 7 Calm2 DHX37 7 Rpl13a-ps1 EP300 7 Reep4 EVI5 7 Ccdc18 EXPH5 7 Itfg1 FASTKD1 7 Lhfpl2 GAPVD1 7 Zfhx4 GOT1 7 Arl6ip1 H3F3B 7 Zbed3 HEXIM1 7 Rab7 HMGB3 7 Nucks1 HMGCR 7 Fam198b HSPA1B 7 Nfe2l1 HSPA5 7 Mat2b HSPH1 7 Tmem138 KIF4A 7 Ccng2 LARP4B 7 Ccng1 LBR 7 Chd2 LIX1L 7 Armcx1 LRIF1 7 Cep128 LUC7L3 7 Dnajc10 MARK2 7 E2f5 MBNL1 7 Chchd6 MIS18BP1 7 Fgfr1op MT-ND1 7 Ppa2 MT-ND2 7 Rbbp6 MT-ND4 7 Acot9 MT-ND4L 7 Rhou MTRNR2L8 7 Rad21 MTRNR2L12 7 Kif14 NFKB1 7 Asxl1 NIPBL 7 Cep110 ODF2 7 Ppp2r5c PARPBP 7 Mesdc2 PCM1 7 Pdha1 PCNT 7 Mapre1 PDE6D 7 Gja1 PICALM 7 Zfand6 POLR2B 7 Cdca3 PRRC2C 7 Terf1 PTPN13 7 Rbms3 PTTG1 7 Slc7a5 PUM1 7 Cpne3 RAB7L1 7 Ptms RAB14 7 Cdc25b RB1CC1 7 Pcf11 RBBP6 7 Ddit4 RBMX 7 Carkd RNF26 7 Ndufc1 RRP15 7 Ncapd2 RSF1 7 Mrpl51 SAPCD2 7 Bola3 SATB2 7 Uhrf2 SEC62 7 Bub1b SENP6 7 Golga5 SESN2 7 Spag5 SETD2 7 Trappc2l SF1 7 Psrc1 SFPQ 7 Dynll1 SLC7A11 7 Vbp1 SLC39A10 7 Gpsm2 SMEK2 7 Ubxn6 SNAPC3 7 Dnajb4 SON 7 Glrx3 SRSF3 7 Sar1a STX18 7 Cenpw TAF7 7 Hn1 TFCP2 7 Odf2 TGS1 7 Atg3 TMEM19 7 Echs1 TOX4 7 Fzd2 UBXN4 7 Arl8b UNKL 7 Hexim1 USP7 7 Pnrc2 VEZF1 7 Atp6ap2 WBP11 7 Cks1b WDR43 7 Unc50 WSB1 7 Akirin2 ZC3H11A 7 Cebpb ZC3H14 7 C330027C09Rik ZNF148 7 Cdc27 ZNF318 7 Cd164 7 F3 7 Pcnp 7 Hp1bp3 7 Nde1 7 Ccdc104 8 Arpc2 8 Snhg3 8 Marcksl1 8 Dhx29 8 Sbno1 8 Dnajc19 8 Socs4 8 Hnrnpc 8 Rps14 8 Gltscr2 8 Ncl 8 Csnk1a1 8 Ercc1 8 Oraov1 8 Ccnd1 8 Myeov2 8 Rala 8 Itga5 8 Serbp1 8 Naca 8 Vim 8 Impact 8 Hnrnpu 8 Snrpa 8 Sox4 8 Pycr2 8 Celf4 8 Srp9 8 Sltm 8 Hspa9 8 Rpl15 8 Pus3 8 Tsc22d1 8 Mrpl21 8 St13 8 Cwc15 8 Gpx7 8 Dhx38 8 Hspb8 8 Timm13 8 Rnf11 8 Snrpd3 8 Arl3 8 Zfp36l2 8 Strap 8 Ddx6 8 Eif2s1 8 Nrbp1 8 Hsp90ab1 8 Zfp36l1 8 Pdcd4 8 Hmgn3 8 Atp5j 8 Ikbkap 8 Tbca 8 Npm1 8 Fth1 8 Banf1 8 Psmc5 8 Hspa4 8 Slc41a1 8 Rpl32 8 Cct8 8 S100a6 8 Gm6563 8 Top1 8 Syncrip 8 Zfc3h1 8 Kdm5b 8 Mrpl38 8 Rps24 8 Gm4204 8 Tes 8 Rpl26 8 Nol8 8 Arf4 8 Tardbp 8 Gnb2l1 8 Nrf1 8 Hsp90aa1 8 Hdgf 8 Stat3 8 Zbtb38 8 Hmga2 8 Nufip2 8 Sh3glb1 8 Irf2bp2 8 Sqstm1 8 Canx 8 Rps21 8 Exo5 8 Ubtd1 8 Hspd1 8 Anp32e 8 Lmna 8 Ogfr 8 Rps3 8 Mex3a 8 Mpp1 8 Pfn1 8 Prrc2c 8 Crlf3 8 Ubtf 8 Bzw1 8 Rpl4 8 Lgals1 8 Actb 8 Ccar1 8 Adar 8 Ddx3x 8 Tlk2 8 Dcun1d5 8 Luzp1 8 Tomm70a 8 Ccdc6 8 Luc7l3 8 Gm9843 8 Rsl1d1 8 Rtn4

TABLE 5 List of highest gene loadings in each of the top 40 principal components from the 44,808 retina STAMPs. Top and bottom genes PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 1 CP ATP1B1 ISL1 PDE6H PRKCA EBF3 SNCG THY1 CBLN2 SLIT2 2 CAR14 SNHG11 TRPM1 ARR3 CCDC136 SLC6A9 NRN1 SLC17A6 C1QL1 TACR3 3 SLC1A3 PAX6 GNG13 GUCA1A KCNE2 LGR5 SLC17A6 NRN1 IGFBP2 NXPH1 4 APOE ELAVL3 VSX2 PDE6C ABLIM1 EBF1 NEFM NELL2 C1QL2 PDE1A 5 CD9 SLC6A1 SCG2 GNAT2 CAR8 PRDM13 MEFL LPL OLFM3 GLRA1 6 COL9A1 GAD1 GPR179 OPN1MW SEBOX ZFP804A FXYD7 TFAP2C TBX3 NETO1 7 RLBP1 VSNL1 PCP2 GNGT2 VSTM2B NFIX RGS4 BHLHE22 GNG2 NTNG1 8 AQP4 STMN2 GRM6 OPN1SW STRIP2 PTPRF NELL2 NPNT CARTPT CDH8 9 ID3 SPOCK3 QPCT RP1 PDE6H PTPRT STMN2 CPLX2 GAP43 ZFHX4 10 SPC25 GAD2 TRNP1 GNB1 ARR3 NEFL CHRNA6 FXYD7 NFIA A330008L17RIK 11 PDPN SPARCL1 NDNF KCNE2 PDE6C NHLH2 THY1 AI593442 MEIS2 TMEFF2 12 CRYM CPLX2 CAR8 THRB OPNIMW LAMP5 RPRM MAF NR4A2 ESAM 13 ABCA8A CDK14 B3GALT2 CNGB3 PCP2 CALB2 ELAVL2 RGS4 COL11A1 PRDM8 14 TIMP3 TFAP2B TGFB2 CST3 LRRTM4 PPP1R17 UCHL1 ALCAM SYT7 SLITRK6 15 HES1 DLGAP1 PRKCA FAM19A3 CEP112 CRABP1 GAP43 CXCL14 2610017I09RIK CACNA2D3 16 CYR61 C1QL1 DKK3 CD59A TP8G SNCG NEFH NECAB1 TFAP2B BHLHE22 17 ZFP36L1 GNG2 FRMD3 MFGE8 ZBTB20 NCKAP5 FSTL1 GAD2 OPTC A730046J19RIK 18 GPR37 TKT SIX3OS1 HOPX ADAMTS5 IER5 KCNIP4 PTN VIP SEBOX 19 SPARC DNER CACNA2D3 BTG2 OPN1SW NEFM CALB2 CRABP1 COL23A1 QPCT 20 ESPN RBFOX1 PAX6 HSPA1A GUCA1A HS6ST2 CDK14 ELAVL2 SLC4A3 GRIK1 −1 SNHG11 GNG13 CLDN5 ABLIM1 SCGN GAD1 NHLH2 1500016L03RIK SLC5A7 CDH9 −2 SCG2 TRPM1 ELTD1 ISL1 A730046J19RIK SLC6A1 SLC6A9 CALB1 GNG7 HS3ST4 −3 ATP1B1 PCP2 CD93 PCP2 CDH8 ID4 NECAB1 TMEFF2 RIMS1 RELN −4 UCHL1 GPR179 PTPRB TRPM1 VSX1 NPNT CRABP1 BAI1 CALB2 NFIA −5 ELAVL3 GRM6 CTLA2A CAR8 PTPRZ1 C1QL2 TFAP2C SLC4A3 NPY PTPRZ1 −6 SPOCK3 ISL1 PLTP GPR179 GSG1 LPL LGR5 SEPT4 CXCL14 BC046251 −7 GABRA1 VSX2 LY6C1 TGFB2 SLIT2 MEIS2 LAMP5 TFAP2B RBFOX1 SLC5A7 −8 VSNL1 TRNP1 RAMP2 PRKCA ZFHX4 C1QL1 PRDM13 SOWAHA IGFBP7 EPHA7 −9 STMN2 CAR8 FAM101B GNG13 GRIK1 SLIT2 NFIX SGK1 NHLH2 SOX6 −10 GAD1 QPCT MGP QPCT PDE1A PCP4L1 IER5 TPM3 PCP4L1 RIMS1 −11 ISL1 FRMD3 RGS5 SPARCL1 NETO1 GAD2 ZFP804A VIM SOX2 NEUROD2 −12 GNG13 SEBOX EGFL7 VSTM2B GABRA1 ZFHX4 PTPRF NPY SCG2 CHODL −13 TRNP1 NDNF GNG11 TRNP1 TACR3 DLGAP1 NCKAP5 TPM3-RS7 ARL4C GNG7 −14 RBFOX1 CACNA2D3 IGFBP7 VSX2 SLITRK6 CXCL14 GRIK2 NEBL PCDH10 GJD2 −15 TFAP2B B3GALT2 SEPP1 GRM6 A330008L17RIK CBLN2 FILIP1L SLC5A7 POMC GABRR2 −16 B3GALT2 STRIP2 VWA1 COL4A1 NXPH1 ALDOC PTPRT NEFH SPOCK3 ISL1 −17 CPLX2 TGFB2 ITM2A CACNA2D3 OTOR RND3 EBF3 GNG7 SPARCL1 COL1A2 −18 FRMD3 GABRR2 COL4A1 COL4A2 CAMK4 SPOCK3 GAD2 C1QL1 ESPN GRM6 −19 GNG2 PRKCA SLC7A5 NDNF ESAM FILIP1L BHLHE22 ZFP804A LPL NDNF −20 PCP4L1 RNF152 FN1 B3GALT2 FEZF2 SCGN NR2F2 FBXW7 CALB1 IGFN1 Top and bottom genes PC11 PC12 PC13 PC14 PC15 PC16 PC17 PC18 PC19 PC20 PC21 1 FOSB CARTPT OPTC VSX1 GNB1 CCK OLFM3 CBLN2 CARTPT OPTC IGF1 2 ZFP36 2610017I09RIK GNB1 RELN RP1 OTOR CAR2 NETO1 NR4A2 ALDH1A1 IGFN1 3 JUNB TFAP2B CST3 CCK CST3 LECT1 LAMP5 SYT6 LRRTM1 ITM2A TFAP2C 4 EGR1 NR4A2 RP1 LECT1 SLC16A1 UNC13C GJD2 CDH9 NFIA SNED1 LAMP5 5 FOS GABRA2 ATP1A2 PCP4L1 HS3ST4 CABP2 DYNC1I1 TACR3 VIP SNCA CARTPT 6 ATF3 CBLN2 SNED1 CDHB S1PR1 GSG1 SLC6A9 NPY RPRM TAC2 CABP2 7 NR4A1 FBXW7 IGFBP2 TNNT1 KCNJ10 COL11A1 GRIA3 HS3ST4 SCG2 PVRL3 PCDH17 8 DUSP1 VIP MEST IGF1 CDH9 C1QL1 TBX3 NFIX 2610017I09RIK LY6E PTPRF 9 IER2 SYT6 FSTL1 IGFN1 ABCA8A SCGN AI593442 NXPH1 LHX4 PTGDS NR2F2 10 KLF4 HPGD IGF2 ZFHX4 BC046251 NHLH2 PTPRF RIMS1 EPHA7 CLDN5 NR4A2 11 PPP1R15A SLC5A7 CDKN1C SCG2 NEUROD2 RELN IGFBP2 COL11A1 NFIX MEST FN1 12 KLF6 NNAT HTRA1 A330008L17RIK WIPI1 TFAP2C THY1 PDE1A GPR22 CTLA2A HS3ST4 13 BTG2 GAD1 PTGDS SIX3OS1 LY6C1 CST3 NEFH C1QL1 TNNT1 IGFBP2 HTRA1 14 CYR61 GRIA3 NXPH1 GJD2 ABCA8B GNB1 DLGAP1 NHLH2 PTPRZ1 RAMP2 SLC6A9 15 NFKBIZ SCG2 WLS RPRM CLDN5 RP1 ABLIM1 GAP43 HS6ST2 VWA1 HS6ST2 16 RP1 GRIK2 PVRL3 UNC13C SPC25 CRABP1 BC046251 GNG7 BHLHE22 LY6C1 SLC17A8 17 GNB1 RIMS1 HSPA1A GJC1 KDR NFIB CNTN4 TBX3 TFAP2C CTSH COL11A1 18 JUN CALB2 SGK1 GNGT2 HSPA1B EBF3 FILIP1L NR2F2 ISL1 SLC7A5 OPTC 19 GM26669 KCND3 HSPA1B LAMP5 NETO1 TBX3 CDKN1C NR4A2 NECAB1 TAC1 NECAB1 20 ADAMTS1 CAR2 ALDH1A1 GNG13 CAV1 A730046I19RIK RBFOX1 CHODL SOX6 PPP1R17 2610017I09RIK −1 OPTC 1500016L03RIK GNGT2 GSG1 OPTC NEUROD2 2610017I09RIK HPGD IGFN1 MGP PPP1R17 −2 CD59A LPL GNAT2 OTOR ATP1A2 NXPH1 KCND3 FBXW7 PCDH17 RGS5 IGFBP5 −3 GNAT2 BHLHE22 FAM19A3 GRIK1 FSTL1 BC046251 IGFBP5 2610017I09RIK IGFBP5 GJC1 SNCA −4 GNGT2 MAF GSG1 FEZF2 IGFBP2 LAMP5 IGFN1 LECT1 IGFBP2 SERPINE2 LECT1 −5 PDE6C CXCL14 LHX4 NNAT ALDH1A1 NFIA NR4A2 UNC13C FN1 CALD1 CCK −6 OPN1MW TFAP2C PDE6C LHX4 ZFP36 NETO1 GRIK2 DNER PPP1R17 RGS4 RGS5 −7 ARR3 NPNT ARR3 SLITRK6 SNED1 TACR3 GABRA2 LAMP5 OLFM3 COL1A2 EBF1 −8 ATP1A2 CPLX2 OPN1MW KCNIP4 PTGDS PDE1A RND3 SHISA9 CABP2 COL4A2 MGP −9 PDE6H SGK1 NNAT NFIB COL11A1 SLIT2 PPP1R17 CCK GABRA1 NR2F2 NEUROD2 −10 NFIB TMEFF2 PDE6H CNTN4 IGFBP7 EPHA7 ALCAM GJD2 HS3ST4 COL4A1 MEIS2 −11 PTGDS TPM3 CNGB3 SOX6 FAM19A3 CDH9 CACNG4 DYNC1I1 RELN IGFN1 IGF2 −12 KCNE2 SOWAHA KCNE2 RP1 GSG1 SOX6 CRABP1 SLC6A9 KCND3 SEPT4 CHODL −13 PTN ARL4C CACNG4 GLRA1 JUNB HPGD CAMK4 MAF WLS COX4I2 CDH8 −14 CLU AI593442 OTOR GNB1 NR4A1 RND3 B230312C02RIK CACNG4 PRDM8 S1PR3 CALD1 −15 FAM19A3 SLC4A3 PTPRZ1 FAM19A3 FOSB WLS 1500016L03RIK SLC17A8 GLRA1 MAF TAC1 −16 OPN1SW TPM3-RS7 KDR VSX2 OTOR SLC17A8 PCDH17 TFAP2B SNCA TFAP2C PRDM13 −17 ENPP2 MEIS2 DNER PCDH10 NBL1 COL1A2 FN1 ALCAM FEZF2 ID4 GJC1 −18 NUDT4 PTN CLDN5 NFIA ATF3 DYNC1I1 CARTPT OPTC AI593442 2610017I09RIK LGR5 −19 SPARC VIM QPN1SW MEST NNAT PCDH10 VIP RGS2 PCDH10 ANXA1 GRIK2 −20 VIM CALB1 VEGFA CST3 NFIB HS3ST4 HS3ST4 NEUROD2 CDH9 ATP1A2 TNNT1 Top and bottom genes PC22 PC23 PC24 PC25 PC26 PC27 PC28 PC29 PC30 PC31 PC32 1 2610017I09RIK IGF2 CARTPT HBB-BS HPGD PPP1R17 CHN2 PTGDS GJD2 PDLIM3 PCDH17 2 NEFH HBA-A1 MAF HBA-A1 IGF2 HBA-A1 RELN GPR22 DYNC1I1 ALDH1A1 PMEPA1 3 C1QL2 HBB-BS PPP1R17 2610017I09RIK IGFBP5 HBB-BS DNER CHN2 NPY RBP1 GSG1 4 IGFBP2 VIP GPR22 TAC2 MT2 IGFN1 GRIK1 TTR CCND1 HOPX PPP1R17 5 THY1 ID4 GNG2 TAC1 CXCL12 EBF1 PCP4L1 GABRA1 FEZF2 ITM2A IGFBP5 6 TBX3 CXCL12 NR4A2 C1QL2 2610017I09RIK NETO1 GPR22 CARTPT SLITRK6 GSTA4 HOPX 7 GAD1 IGFBP5 IGFBP5 GRIK1 TAC2 ALDH1A1 NNAT SYT7 VSNL1 CHN2 BAI1 8 OLFM3 ALDOC GRIK1 CXCL14 LAMP5 VIP DDR1 SCG2 B2M SLC17A8 RBP1 9 KCND3 NR4A2 SLC4A4 NXPH1 MT1 PCP4L1 SLITRK6 DNER ARL4C CCND1 UCHL1 10 NFIX CBLN2 SNED1 B230312C02RIK NETO1 NPNT PMEPA1 SPOCK3 BHLHE22 DBI LHX4 11 DKK3 HS6ST2 CAMK4 LHX2 PTGDS GRIK1 PCDH17 TAC1 2610017I09RIK DAPL1 NFIB 12 PMEPA1 IGFBP2 KCND3 GPR22 IGF1 VSX1 SLC6A1 PRDM13 MT1 RDH10 GAS1 13 NCKAP5 GRIM1 C1QL1 IGF2 CST3 SLITRK6 SHISA9 PTPRT ELAVL2 PRDX6 DDR1 14 ID4 LRRTM1 ID4 LY6C1 LHX4 COL11A1 TAC1 RPRM MT2 GPR22 CALD1 15 SYT7 LECT1 GRIA3 ELAVL2 PDE1A CBLN2 SYT7 SHISA9 PTPRF SBSPON VEGFA 16 SOX6 CHN2 LGR5 OPTC B2M SEPT4 ZBTB20 MAF NPNT NNAT NR2F2 17 HPGD GABRA2 PCP4L1 NFIB ELAVL2 LPL VSTM2B PCDH17 NCKAPS S1PR3 BHLHE22 18 CHODL SLC4A4 DNER NETO1 CHRNA6 COL1A2 PTPRT EBF1 PCDH10 MT2 TBX3 19 SLC17A6 SNED1 NF1A TBX3 NNAT A330008L17RIK TNNT1 SERPINE2 ATF3 ANXA1 NR4A1 20 SIX3OS1 MLC1 IGFN1 NFIX GSG1 NNAT CCK ID4 DNER RPRM ALDH1A1 −1 TAC2 TAC2 HBA-A1 VIP HBB-BS WLS WLS SLC17A8 SLC17A8 GM129 HEXB −2 VIP TAC1 HBB-BS CBLN2 HBA-A1 IGF2 CAR2 PMEPA1 AI593442 ABCA8B SOX6 −3 SYT6 2610017I09RIK PCDH17 WLS WLS PCDH10 PCDH10 LECT1 NXPH1 PTGDS CCK −4 TAC1 CXCL14 CHN2 RND3 PCDH10 PCDH17 PPP1R17 HPGD MAF KLF4 HPGD −5 SNCA C1QL2 SLC6A1 PPP1R17 VIP LECT1 C1QL2 NEUROD2 LHX2 SHISA9 KCNIP4 −6 FXYD6 CDKN1C ELAVL2 CCK CAMK4 CXCL12 RND3 NR2F2 SLC4A4 DIO2 COL11A1 −7 ELAVL2 SNCA PCDH10 RBP1 PRDM8 CAMK4 NXPH1 CBLN2 GRIA3 SNED1 SERPINE2 −8 SERPINE2 ALDH1A1 CBLN2 GNB1 NB4A2 ZFHX4 EBF3 CACNG4 NNAT CRIM1 GABRR2 −9 LAMP5 SLC17A8 TKT CHN2 PPP1R17 TBX3 RBP1 COL11A1 CXCL14 SLITRK2 WLS −10 IGFBP5 SERPINE2 TAC2 UNC13C CCK NFIX B3GALT2 PCDH10 FOS HEXB SLC17A8 −11 GRIA3 PMEPA1 HPGD NR4A2 RNF152 GPR22 OLFM3 GABRA2 GABRA2 GAS1 ATF3 −12 NNAT MGP MGP HS6ST2 CARTPT PRDM8 HPGD RBFOX1 LAMPS TIMP3 SEPP1 −13 STMN2 WLS MEIS2 RP1 GJD2 TAC1 TTR SLITRK6 ALDOC ENPP2 B2M −14 NR4A2 CALD1 GABRA1 HSPA1B CAR2 TACR3 IER5 NFIX CABP2 TTR GRIK1 −15 NECAB1 ELAVL2 SHISA9 SHISA9 CCND1 GLRA1 PTPRZ1 CRIM1 PRDM8 HSPA1B A730046J19RIK −16 B2M HPGD CALD1 VWA1 A730046J19RIK EPHA7 CABP2 VIP NR4A2 NRP1 LECT1 −17 IGFN1 CCND1 UNC13C PCDH10 ALDH1A1 SLC4A4 B2M VEGFA NEUROD2 PPAP2B SLITRK6 −18 CNGB3 S1PR3 RBP1 ALDOC PCDH17 B230312C02RIK CAMK4 IGF2 EGR1 GM26669 CACNA2D3 −19 SLC6A9 AI593442 QPCT SEPT4 NPNT B2M C1QL1 DDR1 SCG2 S1PR1 MAF −20 CALD1 RELN PTGDS TTR NELL2 2610017I09RIK 2610017I09RIK CCK GM13889 PPP1R17 VIP Top and bottom genes PC33 PC34 PC35 PC36 PC37 PC38 PC39 PC40 1 PTPRT ARL4C TTR TPM3-RS7 HEXB TPM3-RS7 CDKN1C SLC17A8 2 PCDH10 RPRM GM129 CDKN1C ATF3 TPM3 HSPA1B GM26669 3 TPBG SLC17A8 GM26669 TBX3 TTR TAC2 HSPA1A ATF3 4 IGFBP5 NPNT PTGDS RND3 PMEPA1 SHISA9 CXCL12 TTR 5 RPRM BHLHE22 KCND3 TPM3 GM26924 RGS2 KLF4 CDKN1C 6 NR2F2 TPM3-RS7 VIP ANGEL2 RBP1 NFKBIA TAC2 CALD1 7 LECT1 CAMK4 IGF2 SYT6 B2M NR2F2 NR4A2 MT2 8 CDK14 TBX3 TPM3-RS7 PCDH17 MAF ELAVL2 GM26924 TAC2 9 DIO2 PTGDS TPM3 TPBG PTGDS IGF1 SHISA9 ADAMTS1 10 TBX3 SLITRK6 CRIM1 IGFN1 MT2 MAF HS6ST2 VSTM2B 11 SLITRK6 TPM3 RBP1 NCKAP5 MFKBIZ PPP1R17 NNAT UTP14B 12 CDKN1C FILIP1L ANGEL2 GRIK2 KCND3 ID4 RELN CXCL12 13 SHISA9 GM26924 ILDR2 NFIX SYT6 HS6ST2 PRDM8 NFKBIZ 14 PTPRF EBF1 SHISA9 NFIB PRDM8 HEXB LRRTM1 CHN2 15 NNAT CHN2 KCNIP4 ALCAM MT1 ILDR2 ID1 DNER 16 CALB2 ELAVL2 TRPM3 CAR2 SEPP1 GM26924 WLS ID4 17 SOX6 PRDM13 TAC2 CHN2 HOPX NEFH LY6C1 NR2E1 18 GABRA2 RBFOX1 WLS CHBNA6 DDR1 NNAT ALCAM GLRA1 19 UNC13C GM13889 GRIK2 NEUROD2 SLC4A4 SYT7 CAR2 NXPH1 20 TAC1 LPL FZD5 GSG1 KLF6 GLRA1 RGS4 SOWAHA −1 TAC2 AI593442 HEXB PTPRT GM129 TAC1 ANDEL2 TPM3-RS7 −2 ELAVL2 SLC4A4 ATF3 TAC2 HSPA1A MT2 SLC17A8 TPM3 −3 GRIK2 VIP HSPA1A RBP1 PVRL3 PTGDS LAMB1 HSPAIB −4 NCKAPS MAF HSPA1B TTR CXCL12 HSPA1A SERPINE2 HSPA1A −5 VIP TAC1 CTSH COL11A1 FOS GRIA3 NPY EGR1 −6 NFIX COL11A1 IER5 PMEPA1 NPY HSPA1B TAC1 OLFM3 −7 SCG2 RND3 NFKBIA PTPRF SOX6 VIP VIP JUND −8 KCND3 LRRTM1 RGS2 WLS OPTC GM13889 NFIB PTGDS −9 PRDM8 IGF1 PCDH17 EPHA7 COL11A1 MT1 PCDH17 FOS −10 CHN2 GAD2 SEPP1 SERPINE2 HTRA1 A330069E16RIK IGFBP5 SLIT2 −11 ATP1B3 SYT6 PPP1R17 CAMK4 PPP1R15A CXCL12 A330069E16RIK FEZF2 −12 NPY CXCL14 SGK1 PDLIM3 PPP1R17 KLF4 CARTPT DDR1 −13 MAF TPBG GPR22 HOPX NBL1 PCDH10 HEXB SIX3OS1 −14 TFAP2B CHRNA6 LHX2 BAI1 DUSP6 CDKN1C GPR22 HOPX −15 NEUROD2 SERPINE2 SERPINE2 LRRTM1 A330069E16RIK TKT CNGB3 TAC1 −16 CRABP1 CBLN2 DDR1 CARTPT CROT TPBG VWA1 CBLN2 −17 ALDOC PDLIM3 SLC17A8 IGFBP2 HSPA1B KCND3 GM129 PDLIM3 −18 NPNT SCG2 SAT1 NPY PTN CHRNA6 CABP2 RPRM −19 FXYD6 HS6ST2 PON2 A730046J19RIK PTPRT NHLH2 FZD5 SLC6A9 −20 DKK3 PTPRF B2M HSPA1B GPX8 BHLHE22 GRIA3 PDE1A

TABLE 6 Genes differentially expressed in each of the 39 retinal cell clusters. myAUC myDiff power cluster # cluster no. 1 DE = 190 CALB1 0.966 3.615047 0.466 1 SLC4A3 0.963 3.448571 0.463 1 TPM3 0.965 3.151521 0.465 1 SEPT4 0.964 2.939258 0.464 1 VIM 0.944 2.937992 0.444 1 SEPT7 0.968 2.808893 0.468 1 1500016L03RIK 0.896 2.777389 0.396 1 LHX1 0.862 2.524691 0.362 1 ATP1B1 0.913 2.520540 0.413 1 BAI1 0.855 2.451809 0.355 1 CD47 0.904 2.425913 0.404 1 TPM3-RS7 0.850 2.340003 0.350 1 SNHG11 0.906 2.329016 0.406 1 PCSK1N 0.910 2.295309 0.410 1 C1QL1 0.863 2.257023 0.363 1 PPP1R1A 0.872 2.200677 0.372 1 NEBL 0.840 2.187973 0.340 1 MAGED1 0.901 2.143543 0.401 1 GNAS 0.936 2.121058 0.436 1 PCBD1 0.837 2.100263 0.337 1 TMEFF2 0.837 2.087888 0.337 1 SMARCA4 0.907 2.073006 0.407 1 LRRC4 0.833 2.057230 0.333 1 UTRN 0.803 1.995075 0.303 1 ADRA2A 0.813 1.993091 0.313 1 TFAP2B 0.899 1.986766 0.399 1 MYO6 0.860 1.972649 0.360 1 NDRG4 0.882 1.970533 0.382 1 GNG2 0.825 1.959108 0.325 1 TMEM132A 0.816 1.954705 0.316 1 GM16551 0.799 1.945718 0.299 1 ONECUT2 0.807 1.931103 0.307 1 NDRG1 0.906 1.920706 0.406 1 A330050F15RIK 0.804 1.915932 0.304 1 TKT 0.855 1.910653 0.355 1 COL27A1 0.726 1.883251 0.226 1 SGK1 0.821 1.876982 0.321 1 FAM126A 0.802 1.858034 0.302 1 WNK4 0.784 1.841538 0.284 1 TAGLN3 0.815 1.782407 0.315 1 SLC12A2 0.803 1.768314 0.303 1 SLC4A5 0.781 1.760906 0.281 1 LSAMP 0.829 1.738595 0.329 1 SYT2 0.779 1.713377 0.279 1 LY6E 0.747 1.701416 0.247 1 STMN2 0.827 1.697169 0.327 1 LMO1 0.769 1.657498 0.269 1 SEPT8 0.784 1.654456 0.284 1 PROX1 0.846 1.646287 0.346 1 CHGB 0.841 1.628412 0.341 1 NPY 0.737 1.627193 0.237 1 GALNT18 0.765 1.620340 0.265 1 ZEB2 0.793 1.616501 0.293 1 SOWAHA 0.752 1.605413 0.252 1 LIMA1 0.773 1.599290 0.273 1 THRSP 0.758 1.592738 0.258 1 MEGF11 0.765 1.587717 0.265 1 UCHL1 0.809 1.585799 0.309 1 F2R 0.742 1.585087 0.242 1 RCN2 0.798 1.581440 0.298 1 VWC2 0.763 1.571960 0.263 1 PCSK6 0.735 1.571878 0.235 1 ITGB5 0.745 1.557512 0.245 1 APP 0.822 1.550700 0.322 1 TUBB2A 0.817 1.540466 0.317 1 BC030476 0.750 1.535140 0.250 1 CDC42EP4 0.754 1.512842 0.254 1 PTPRO 0.748 1.502980 0.248 1 RGS3 0.746 1.501006 0.246 1 2410066E13RIK 0.768 1.487613 0.268 1 WFDC10 0.718 1.485101 0.218 1 ANK2 0.855 1.477172 0.355 1 CTTNBP2 0.741 1.474312 0.241 1 FAM124A 0.721 1.474108 0.221 1 TNR 0.729 1.463381 0.229 1 RBFOX2 0.768 1.456189 0.268 1 SPARCL1 0.767 1.446874 0.267 1 THSD7A 0.783 1.441073 0.283 1 PACSIN1 0.799 1.440395 0.299 1 VAT1L 0.751 1.429302 0.251 1 SYT11 0.786 1.425350 0.286 1 AKAP12 0.739 1.424278 0.239 1 ABHD10 0.763 1.411246 0.263 1 PTPRT 0.729 1.406432 0.229 1 RCAN2 0.754 1.405642 0.254 1 KIF3A 0.793 1.398151 0.293 1 LRP11 0.758 1.397326 0.258 1 RTN1 0.801 1.393281 0.301 1 FKBP3 0.807 1.383785 0.307 1 NEFL 0.814 1.374162 0.314 1 CD59A 0.753 1.372191 0.253 1 CDH4 0.748 1.371678 0.248 1 TMOD1 0.746 1.367990 0.246 1 FAIM2 0.751 1.367737 0.251 1 CTNNA2 0.739 1.362929 0.239 1 SEPT6 0.737 1.357596 0.237 1 MAB21L2 0.751 1.352143 0.251 1 MSI2 0.844 1.351412 0.344 1 ONECUT1 0.723 1.348846 0.223 1 ANGPT2 0.716 1.342637 0.216 1 THSD7B 0.709 1.318613 0.209 1 SNAP25 0.905 1.316286 0.405 1 NEFM 0.766 1.311134 0.266 1 SCD2 0.753 1.296970 0.253 1 FAM84B 0.734 1.296355 0.234 1 MGARP 0.888 1.277813 0.388 1 APPL2 0.758 1.261116 0.258 1 DNER 0.752 1.256005 0.252 1 PFKFB3 0.706 1.250256 0.206 1 MT1 0.729 1.246724 0.229 1 LMO4 0.742 1.245222 0.242 1 ZFP804A 0.746 1.241753 0.246 1 RABEP1 0.771 1.228045 0.271 1 OSBPL1A 0.729 1.227105 0.229 1 YWHAG 0.763 1.225112 0.263 1 PDE3A 0.702 1.219989 0.202 1 CACNG3 0.717 1.219146 0.217 1 REEP5 0.751 1.204753 0.251 1 KLF13 0.706 1.196781 0.206 1 TMX4 0.753 1.186779 0.253 1 SNCG 0.712 1.184574 0.212 1 SNRPN 0.732 1.180677 0.232 1 SLC24A2 0.705 1.172493 0.205 1 GNAI1 0.726 1.153326 0.226 1 MLLT11 0.733 1.153193 0.233 1 DST 0.742 1.150327 0.242 1 ADARB1 0.742 1.147777 0.242 1 ANKRD29 0.706 1.145796 0.206 1 ST8SIA3 0.703 1.129373 0.203 1 PLCB4 0.765 1.116768 0.265 1 BEX2 0.762 1.114780 0.262 1 FAM115A 0.746 1.114026 0.246 1 PLEKHA1 0.751 1.113187 0.251 1 MPC1 0.706 1.109670 0.206 1 MOCS2 0.739 1.107821 0.239 1 COX5A 0.776 1.104444 0.276 1 TUBA1A 0.774 1.100378 0.274 1 PLCH1 0.705 1.097744 0.205 1 PIK3R3 0.711 1.092873 0.211 1 TSPAN3 0.771 1.087383 0.271 1 EMC9 0.703 1.086119 0.203 1 UHRF1BP1L 0.710 1.081116 0.210 1 NAV1 0.713 1.074276 0.213 1 INA 0.724 1.066690 0.224 1 HAUS8 0.708 1.065310 0.208 1 HSP90AB1 0.800 1.059681 0.300 1 NDN 0.733 1.058386 0.233 1 NEFH 0.707 1.052242 0.207 1 GATSL2 0.702 1.046289 0.202 1 TPM1 0.728 1.044557 0.228 1 STMN3 0.743 1.042409 0.243 1 ZWINT 0.717 1.028737 0.217 1 SPOCK3 0.704 1.026265 0.204 1 ELAVL3 0.730 1.019721 0.230 1 ATP6V1A 0.761 1.013906 0.261 1 LDHA 0.298 −1.429546 0.202 1 H3F3B 0.226 −1.724698 0.274 1 EPB4.1 0.297 −1.890330 0.203 1 A930011O12RIK 0.289 −1.908058 0.211 1 TMA7 0.292 −1.922734 0.208 1 CRX 0.295 −1.940202 0.205 1 HMGN1 0.173 −2.030775 0.327 1 MPP4 0.297 −2.122800 0.203 1 CNGB1 0.289 −2.144480 0.211 1 FAM57B 0.269 −2.148614 0.231 1 GUCA1B 0.298 −2.192529 0.202 1 AIPL1 0.269 −2.202228 0.231 1 PDE6A 0.284 −2.233229 0.216 1 RDH12 0.291 −2.272536 0.209 1 GNB1 0.187 −2.284490 0.313 1 NEUROD1 0.238 −2.422956 0.262 1 NRL 0.224 −2.424409 0.276 1 UNC119 0.193 −2.478130 0.307 1 NR2E3 0.217 −2.484357 0.283 1 RS1 0.222 −2.534411 0.278 1 SLC24A1 0.230 −2.558786 0.270 1 PRPH2 0.154 −2.572327 0.346 1 ROM1 0.184 −2.594330 0.316 1 RP1 0.190 −2.660436 0.310 1 PDE6B 0.190 −2.707960 0.310 1 TULP1 0.163 −2.748272 0.337 1 CNGA1 0.215 −2.752815 0.285 1 RCVRN 0.175 −2.769719 0.325 1 PDE6G 0.160 −2.791625 0.340 1 PDC 0.133 −2.805456 0.367 1 GNGT1 0.123 −2.821179 0.377 1 RPGRIP1 0.195 −2.867157 0.305 1 GNAT1 0.158 −2.923872 0.342 1 RHO 0.121 −2.940345 0.379 1 SAG 0.118 −2.967888 0.382 1 cluster no. 2 DE = 174 NEFL 0.984 3.829399 0.484 2 NEFM 0.953 3.464532 0.453 2 SNCG 0.938 3.269859 0.438 2 CALB2 0.884 3.081448 0.384 2 STMN2 0.944 2.861225 0.444 2 THY1 0.900 2.782679 0.400 2 ATP1B1 0.916 2.633335 0.416 2 SLC17A6 0.879 2.610603 0.379 2 NRN1 0.868 2.509114 0.368 2 UCHL1 0.909 2.411926 0.409 2 GAP43 0.867 2.314068 0.367 2 STMN3 0.906 2.200448 0.406 2 CDK14 0.855 2.189091 0.355 2 YWHAH 0.854 2.103748 0.354 2 RGS4 0.775 2.052411 0.275 2 NELL2 0.801 2.005519 0.301 2 SNHG11 0.847 1.998298 0.347 2 RTN1 0.872 1.992219 0.372 2 FXYD7 0.815 1.921975 0.315 2 INA 0.857 1.864647 0.357 2 TPPP3 0.789 1.858532 0.289 2 TUBB2A 0.851 1.844621 0.351 2 RBPMS 0.796 1.835589 0.296 2 MEG3 0.835 1.831667 0.335 2 SCN2A1 0.798 1.825259 0.298 2 TUBB3 0.814 1.819493 0.314 2 VSNL1 0.793 1.812314 0.293 2 APP 0.848 1.800057 0.348 2 MFSD6 0.791 1.774345 0.291 2 OLFM1 0.832 1.767142 0.332 2 CEND1 0.806 1.753636 0.306 2 KIF5A 0.806 1.715671 0.306 2 ZWINT 0.822 1.713431 0.322 2 BASP1 0.839 1.707778 0.339 2 CHRNA6 0.751 1.703049 0.251 2 NAP1L5 0.826 1.688741 0.326 2 SCN1A 0.761 1.675414 0.261 2 SPARCL1 0.806 1.650738 0.306 2 RAB6B 0.826 1.648695 0.326 2 SNCA 0.746 1.628302 0.246 2 DNER 0.806 1.625146 0.306 2 MYT1L 0.782 1.602185 0.282 2 TAGLN3 0.789 1.596353 0.289 2 NSG2 0.791 1.591428 0.291 2 NDRG4 0.818 1.579659 0.318 2 KCNIP4 0.724 1.575295 0.224 2 MAP1A 0.761 1.564301 0.261 2 FGF12 0.759 1.554984 0.259 2 CPLX2 0.757 1.547165 0.257 2 LSAMP 0.764 1.532664 0.264 2 NSG1 0.773 1.531646 0.273 2 GNG3 0.798 1.526804 0.298 2 TTC3 0.863 1.526759 0.363 2 SNRPN 0.786 1.524628 0.286 2 MGST3 0.763 1.521974 0.263 2 POU4F1 0.708 1.493041 0.208 2 RBFOX1 0.756 1.490707 0.256 2 2900011O08RIK 0.797 1.489750 0.297 2 S100A10 0.739 1.487422 0.239 2 CALM2 0.848 1.470176 0.348 2 CPLX1 0.711 1.458879 0.211 2 CAMK2N1 0.791 1.455445 0.291 2 GABBR2 0.734 1.435871 0.234 2 RBPMS2 0.735 1.422357 0.235 2 ELAVL2 0.716 1.416182 0.216 2 REEP5 0.767 1.411279 0.267 2 ACOT7 0.763 1.408963 0.263 2 LYNX1 0.732 1.398066 0.232 2 CHRNB3 0.724 1.396429 0.224 2 RAB6A 0.802 1.365048 0.302 2 SYT11 0.789 1.361853 0.289 2 RPH3A 0.769 1.361064 0.269 2 MGLL 0.731 1.351262 0.231 2 CAPNS1 0.766 1.336082 0.266 2 ELAVL4 0.739 1.327648 0.239 2 MLLT11 0.754 1.324574 0.254 2 APBB2 0.733 1.324301 0.233 2 HPCA 0.735 1.312442 0.235 2 PPP2R2C 0.729 1.312231 0.229 2 MYO1B 0.703 1.310809 0.203 2 PCDHA2 0.752 1.310031 0.252 2 SULT4A1 0.720 1.305228 0.220 2 ROBO2 0.735 1.276553 0.235 2 ATL1 0.728 1.276524 0.228 2 YWHAB 0.828 1.272542 0.328 2 BEND6 0.719 1.270603 0.219 2 AHNAK2 0.713 1.266931 0.213 2 TUBA1A 0.825 1.258349 0.325 2 RESP18 0.702 1.244231 0.202 2 NRXN1 0.719 1.242874 0.219 2 ATP2B2 0.719 1.240608 0.219 2 EPHA5 0.723 1.231067 0.223 2 SPOCK2 0.735 1.228244 0.235 2 TMEM130 0.726 1.225743 0.226 2 YWHAG 0.751 1.224966 0.251 2 SRGAP1 0.707 1.220082 0.207 2 STMN4 0.722 1.214691 0.222 2 GNAS 0.823 1.206586 0.323 2 EBF1 0.717 1.202313 0.217 2 KIF5C 0.748 1.199040 0.248 2 TPM1 0.735 1.195887 0.235 2 TTLL7 0.707 1.194259 0.207 2 HSP90AB1 0.844 1.192653 0.344 2 ENO2 0.784 1.190777 0.284 2 INPP5F 0.710 1.175178 0.210 2 L1CAM 0.714 1.174820 0.214 2 SERINC1 0.776 1.172132 0.276 2 KIFAP3 0.781 1.169721 0.281 2 TMSB10 0.748 1.167262 0.248 2 ATPIF1 0.773 1.160103 0.273 2 MAPT 0.751 1.153592 0.251 2 EMB 0.704 1.153408 0.204 2 SYN2 0.713 1.152558 0.213 2 CALM3 0.757 1.147375 0.257 2 SCG2 0.767 1.144454 0.267 2 RAB3C 0.735 1.143869 0.235 2 TMOD2 0.733 1.143826 0.233 2 PCP4 0.743 1.137348 0.243 2 LDHB 0.729 1.136283 0.229 2 OGFRL1 0.728 1.132671 0.228 2 PLS3 0.701 1.129242 0.201 2 OSBPL1A 0.713 1.127818 0.213 2 SYT4 0.736 1.109372 0.236 2 CD47 0.749 1.108135 0.249 2 CNTN1 0.716 1.100946 0.216 2 SPOCK3 0.713 1.096385 0.213 2 KLC1 0.761 1.081218 0.261 2 DPYSL2 0.722 1.070807 0.222 2 CBX6 0.706 1.069450 0.206 2 GNAO1 0.801 1.066166 0.301 2 RBFOX3 0.706 1.062023 0.206 2 SEPT3 0.710 1.061409 0.210 2 RTN3 0.764 1.054404 0.264 2 TXN1 0.741 1.045930 0.241 2 CYGB 0.712 1.041602 0.212 2 DSTN 0.736 1.028947 0.236 2 NEFH 0.701 1.028807 0.201 2 EPB4.1L3 0.735 1.024561 0.235 2 NDN 0.729 1.022810 0.229 2 YWHAQ 0.735 1.021231 0.235 2 ATP6V1G2 0.713 1.019868 0.213 2 CYB5R3 0.702 1.016407 0.202 2 GPRASP1 0.742 1.013893 0.242 2 RIT2 0.711 1.012204 0.211 2 PDCD4 0.741 1.004699 0.241 2 H3F3B 0.271 −1.176930 0.229 2 DDX5 0.276 −1.193109 0.224 2 GNB1 0.239 −1.628273 0.261 2 TMA7 0.290 −1.756221 0.210 2 PDE6A 0.298 −1.916518 0.202 2 RDH12 0.299 −1.978256 0.201 2 NEUROD1 0.265 −1.982771 0.235 2 AIPL1 0.277 −2.036910 0.223 2 NRL 0.241 −2.048768 0.259 2 CRX 0.293 −2.064793 0.207 2 CNGA1 0.239 −2.128658 0.261 2 RS1 0.239 −2.132605 0.261 2 UNC119 0.212 −2.193079 0.288 2 HMGN1 0.156 −2.204076 0.344 2 ROM1 0.206 −2.223073 0.294 2 SLC24A1 0.243 −2.273294 0.257 2 NR2E3 0.229 −2.289315 0.271 2 TULP1 0.174 −2.369311 0.326 2 PDE6B 0.202 −2.391414 0.298 2 PDE6G 0.180 −2.394168 0.320 2 RP1 0.203 −2.416303 0.297 2 PRPH2 0.164 −2.440696 0.336 2 RCVRN 0.183 −2.450023 0.317 2 GNAT1 0.175 −2.524310 0.325 2 RHO 0.130 −2.595284 0.370 2 SAG 0.129 −2.599480 0.371 2 GNGT1 0.129 −2.621825 0.371 2 RPGRIP1 0.204 −2.684191 0.296 2 PDC 0.139 −2.696102 0.361 2 cluster no. 3 DE = 162 RIMS1 0.992 4.082215 0.492 3 CALB2 0.959 3.407422 0.459 3 SCG2 0.951 2.785881 0.451 3 NPY 0.904 2.685796 0.404 3 SPOCK3 0.945 2.678047 0.445 3 SNHG11 0.942 2.664892 0.442 3 SLC5A7 0.889 2.523739 0.389 3 GAD1 0.893 2.305332 0.393 3 PCP4 0.927 2.304931 0.427 3 ATP1B1 0.915 2.244273 0.415 3 GNG7 0.872 2.199902 0.372 3 SPARCL1 0.877 2.152659 0.377 3 CHAT 0.839 2.117764 0.339 3 IGFBP7 0.874 2.106632 0.374 3 KCNC1 0.862 2.034054 0.362 3 CXCL14 0.836 2.027676 0.336 3 RBFOX1 0.842 2.010200 0.342 3 NHLH2 0.857 1.965244 0.357 3 PCP4L1 0.858 1.946188 0.358 3 HECW1 0.840 1.932796 0.340 3 RGS7BP 0.817 1.924553 0.317 3 MEGF11 0.822 1.915714 0.322 3 LSAMP 0.846 1.876113 0.346 3 GABRD 0.818 1.867550 0.318 3 CACNA2D1 0.817 1.822163 0.317 3 ID4 0.811 1.814870 0.311 3 CMTM8 0.807 1.803043 0.307 3 KCNAB1 0.797 1.796360 0.297 3 PPFIBP1 0.812 1.772586 0.312 3 ZMAT4 0.809 1.764427 0.309 3 TGFB3 0.799 1.762589 0.299 3 RPH3A 0.864 1.751654 0.364 3 NNAT 0.826 1.742048 0.326 3 CALB1 0.822 1.723125 0.322 3 CACNG2 0.801 1.702459 0.301 3 CALM1 0.934 1.694273 0.434 3 PCDH10 0.781 1.688172 0.281 3 PAPPA2 0.743 1.682248 0.243 3 SOX2OT 0.798 1.681475 0.298 3 SCG3 0.850 1.653641 0.350 3 DLGAP1 0.805 1.626709 0.305 3 CHN1 0.835 1.617582 0.335 3 GPR123 0.778 1.617023 0.278 3 FAM184B 0.787 1.601364 0.287 3 SLC32A1 0.796 1.599822 0.296 3 COL25A1 0.764 1.584211 0.264 3 PPM1L 0.775 1.568651 0.275 3 CHGB 0.881 1.563185 0.381 3 MEG3 0.866 1.563114 0.366 3 GABRA2 0.758 1.561233 0.258 3 CNTNAP2 0.811 1.558861 0.311 3 LIN7A 0.837 1.506146 0.337 3 CAMK2N1 0.830 1.503683 0.330 3 A830010M20R1K 0.761 1.495505 0.261 3 APBA1 0.756 1.494915 0.256 3 CPLX2 0.795 1.493169 0.295 3 MAGI3 0.762 1.479676 0.262 3 CTTNBP2 0.780 1.474337 0.280 3 SLC6A1 0.797 1.471722 0.297 3 TFAP2B 0.838 1.458329 0.338 3 GABRA4 0.731 1.443690 0.231 3 ISL1 0.866 1.442516 0.366 3 FAM49B 0.785 1.430077 0.285 3 CAMK2A 0.736 1.425387 0.236 3 CDK14 0.773 1.414271 0.273 3 GSTO1 0.715 1.408011 0.215 3 GRIA3 0.746 1.402325 0.246 3 TENM2 0.740 1.390000 0.240 3 CAPZA2 0.805 1.363952 0.305 3 TAGLN3 0.781 1.361440 0.281 3 SYT11 0.787 1.343219 0.287 3 GALNT15 0.718 1.338314 0.218 3 MAPK10 0.747 1.333658 0.247 3 SOX2 0.748 1.328242 0.248 3 GRIA2 0.810 1.314674 0.310 3 SNRPN 0.765 1.302095 0.265 3 STXBP6 0.715 1.300343 0.215 3 PSD3 0.724 1.295147 0.224 3 BASP1 0.786 1.289016 0.286 3 ARL4C 0.730 1.279132 0.230 3 SYNPR 0.776 1.278017 0.276 3 HLF 0.782 1.276773 0.282 3 NAP1L5 0.796 1.275991 0.296 3 APP 0.736 1.275816 0.236 3 NREP 0.818 1.271487 0.318 3 PTPRD 0.801 1.264783 0.301 3 NRCAM 0.742 1.263960 0.242 3 CD47 0.788 1.255114 0.288 3 PODXL2 0.767 1.235972 0.267 3 STMN3 0.779 1.235054 0.279 3 NEFH 0.713 1.230658 0.213 3 DAPK1 0.726 1.224896 0.226 3 ELAVL3 0.770 1.220472 0.270 3 VSTM2A 0.709 1.220317 0.209 3 REEP5 0.747 1.212653 0.247 3 CYFIP2 0.737 1.198555 0.237 3 AMIGO2 0.719 1.193345 0.219 3 GNG3 0.783 1.192467 0.283 3 CHD3 0.758 1.190095 0.258 3 DTNB 0.717 1.187726 0.217 3 NPTN 0.778 1.186421 0.278 3 DIRAS2 0.721 1.182766 0.221 3 PGM2L1 0.750 1.178870 0.250 3 KIF5C 0.760 1.178481 0.260 3 SYT1 0.855 1.177984 0.355 3 LDHB 0.778 1.172023 0.278 3 ELMOD1 0.748 1.164081 0.248 3 PLCH1 0.704 1.162078 0.204 3 EDIL3 0.725 1.160835 0.225 3 NRXN2 0.766 1.157403 0.266 3 FAM115A 0.738 1.155208 0.238 3 MED12L 0.710 1.151691 0.210 3 MXRA7 0.776 1.145751 0.276 3 DNM3 0.796 1.143089 0.296 3 VSTM2L 0.703 1.141293 0.203 3 1700025G04R1K 0.723 1.129913 0.223 3 ATP2B2 0.721 1.129631 0.221 3 SNCB 0.786 1.128583 0.286 3 TTC3 0.820 1.121625 0.320 3 SV2A 0.778 1.119631 0.278 3 MGLL 0.731 1.117164 0.231 3 ESPN 0.725 1.107524 0.225 3 FEZ1 0.713 1.105736 0.213 3 CELF4 0.802 1.102736 0.302 3 TMEM191C 0.709 1.102454 0.209 3 PRAF2 0.719 1.093227 0.219 3 CYGB 0.729 1.086962 0.229 3 PCDHA2 0.724 1.084084 0.224 3 GPM6A 0.774 1.076995 0.274 3 SEPT11 0.701 1.075883 0.201 3 ZCCHC18 0.727 1.075250 0.227 3 6430548M08RIK 0.736 1.071386 0.236 3 ITM2C 0.754 1.051279 0.254 3 ATP6V1E1 0.784 1.048681 0.284 3 SLC4A10 0.714 1.048067 0.214 3 GABRB3 0.707 1.045363 0.207 3 HPCAL1 0.723 1.028678 0.223 3 CACNA2D2 0.710 1.018877 0.210 3 YWHAH 0.728 1.009599 0.228 3 CST3 0.282 −1.475405 0.218 3 GNB1 0.240 −1.654043 0.260 3 HMGN1 0.189 −1.827649 0.311 3 AIPL1 0.290 −1.857153 0.210 3 RCVRN 0.207 −2.042189 0.293 3 UNC119 0.221 −2.055898 0.279 3 NRL 0.242 −2.067154 0.258 3 CNGA1 0.240 −2.096207 0.260 3 ROM1 0.209 −2.116826 0.291 3 NR2E3 0.240 −2.136288 0.260 3 PDC 0.166 −2.152007 0.334 3 PDE6G 0.192 −2.152778 0.308 3 PDE6B 0.213 −2.158794 0.287 3 SLC24A1 0.253 −2.169851 0.247 3 RP1 0.215 −2.179412 0.285 3 TULP1 0.186 −2.181446 0.314 3 RPGRIP1 0.226 −2.203667 0.274 3 RS1 0.237 −2.206460 0.263 3 PRPH2 0.177 −2.226499 0.323 3 GNGT1 0.154 −2.289551 0.346 3 GNAT1 0.187 −2.336430 0.313 3 SAG 0.143 −2.366434 0.357 3 RHO 0.148 −2.382665 0.352 3 cluster no. 4 DE = 84 TAC1 0.957 3.797157 0.457 4 CALB2 0.901 2.593063 0.401 4 SNHG11 0.924 2.325381 0.424 4 IGFBP7 0.837 2.280199 0.337 4 PAX6 0.913 2.258708 0.413 4 NHLH2 0.869 2.201437 0.369 4 GRIA2 0.915 2.170104 0.415 4 AI593442 0.810 2.066669 0.310 4 PCP4 0.892 2.063350 0.392 4 SPOCK3 0.845 2.017115 0.345 4 COL25A1 0.778 1.916207 0.278 4 KCTD12 0.742 1.898538 0.242 4 CXCL14 0.765 1.846094 0.265 4 OGFRL1 0.824 1.840851 0.324 4 GBX2 0.726 1.819879 0.226 4 LHX9 0.757 1.816715 0.257 4 KCNIP4 0.751 1.748102 0.251 4 TKT 0.815 1.737069 0.315 4 PCDH8 0.704 1.720415 0.204 4 CELF4 0.896 1.718605 0.396 4 STMN2 0.794 1.687253 0.294 4 MEG3 0.889 1.662832 0.389 4 DNER 0.808 1.653824 0.308 4 ZFHX3 0.765 1.644741 0.265 4 A830036E02RIK 0.710 1.606762 0.210 4 SIX6 0.755 1.580762 0.255 4 NDRG4 0.824 1.563205 0.324 4 HLF 0.782 1.551737 0.282 4 GRIN2B 0.702 1.522238 0.202 4 SNCA 0.734 1.483602 0.234 4 SERPINI1 0.734 1.415131 0.234 4 LY6H 0.701 1.377466 0.201 4 GRIA4 0.724 1.373989 0.224 4 SPARCL1 0.724 1.358443 0.224 4 NSG2 0.727 1.353166 0.227 4 CDK14 0.720 1.340365 0.220 4 SCN3A 0.708 1.309240 0.208 4 NRXN2 0.734 1.297254 0.234 4 NAV1 0.714 1.289989 0.214 4 ATP1B1 0.800 1.284113 0.300 4 STXBP5 0.719 1.259255 0.219 4 ELAVL3 0.761 1.253246 0.261 4 NUDT4 0.751 1.236266 0.251 4 CALM1 0.881 1.220586 0.381 4 PNMAL2 0.728 1.206131 0.228 4 APP 0.774 1.200908 0.274 4 TTC3 0.829 1.190737 0.329 4 BASP1 0.744 1.183024 0.244 4 RPH3A 0.717 1.156227 0.217 4 CYGB 0.704 1.143763 0.204 4 GPM6A 0.730 1.143690 0.230 4 AGAP1 0.713 1.142972 0.213 4 AUTS2 0.704 1.127089 0.204 4 RTN1 0.767 1.123584 0.267 4 SLC6A1 0.704 1.115752 0.204 4 SLC22A17 0.712 1.112067 0.212 4 SOX4 0.725 1.096108 0.225 4 ANK3 0.747 1.082388 0.247 4 NAP1L5 0.711 1.054049 0.211 4 CALM2 0.785 1.011094 0.285 4 MARCKSL1 0.711 1.007890 0.211 4 LDHA 0.288 −1.329895 0.212 4 HMGN1 0.234 −1.362895 0.266 4 UNC119 0.256 −1.364415 0.244 4 NEUROD1 0.269 −1.652305 0.231 4 GNB1 0.221 −1.671553 0.279 4 SLC24A1 0.275 −1.699003 0.225 4 RS1 0.266 −1.730768 0.234 4 RPGRIP1 0.250 −1.738476 0.250 4 TULP1 0.212 −1.762716 0.288 4 NR2E3 0.250 −1.799965 0.250 4 GNAT1 0.216 −1.817149 0.284 4 CNGA1 0.253 −1.822516 0.247 4 NRL 0.252 −1.843815 0.248 4 RCVRN 0.213 −1.877735 0.287 4 PRPH2 0.190 −1.894117 0.310 4 RHO 0.169 −1.917425 0.331 4 ROM1 0.213 −1.930023 0.287 4 RP1 0.231 −1.971244 0.269 4 PDE6G 0.206 −2.001563 0.294 4 SAG 0.159 −2.004070 0.341 4 PDE6B 0.223 −2.036922 0.277 4 GNGT1 0.164 −2.084646 0.336 4 PDC 0.163 −2.170946 0.337 4 cluster no. 5 DE = 159 CALB2 0.823 3.123037 0.323 5 TAC1 0.833 2.626378 0.333 5 TPBG 0.876 2.533358 0.376 5 C1QL1 0.924 2.527843 0.424 5 CXCL14 0.901 2.230271 0.401 5 SYNPR 0.925 2.131719 0.425 5 STMN2 0.886 2.086199 0.386 5 PCDH10 0.797 2.043265 0.297 5 SNHG11 0.922 2.035822 0.422 5 NRXN3 0.923 2.007402 0.423 5 CHGB 0.916 2.006283 0.416 5 DLGAP1 0.862 1.951491 0.362 5 GAD1 0.895 1.927132 0.395 5 SLC6A1 0.882 1.917232 0.382 5 ATP1B1 0.889 1.878433 0.389 5 GRIA3 0.852 1.861206 0.352 5 AI593442 0.831 1.830170 0.331 5 PAX6 0.867 1.815993 0.367 5 MEIS2 0.888 1.783257 0.388 5 DTNBP1 0.850 1.781289 0.350 5 MEG3 0.905 1.740870 0.405 5 SLC32A1 0.859 1.720626 0.359 5 CD47 0.872 1.714293 0.372 5 LSAMP 0.847 1.699605 0.347 5 2900011O08RIK 0.840 1.682621 0.340 5 RPH3A 0.865 1.676398 0.365 5 NRXN2 0.862 1.671095 0.362 5 ZFHX3 0.794 1.649873 0.294 5 CDK5R1 0.856 1.647661 0.356 5 GAD2 0.798 1.638829 0.298 5 FILIP1L 0.769 1.637232 0.269 5 B2M 0.800 1.608359 0.300 5 P2RY1 0.777 1.585637 0.277 5 NSG2 0.825 1.585339 0.325 5 OGFRL1 0.850 1.573178 0.350 5 STMN1 0.823 1.572466 0.323 5 C1QL2 0.769 1.565457 0.269 5 ZEB2 0.831 1.544523 0.331 5 NHLH2 0.808 1.538909 0.308 5 SYT7 0.808 1.527501 0.308 5 RGS8 0.796 1.505359 0.296 5 ELAVL3 0.838 1.485639 0.338 5 UACA 0.774 1.475738 0.274 5 SYT6 0.747 1.459682 0.247 5 CPLX2 0.827 1.458139 0.327 5 FRMD5 0.787 1.433194 0.287 5 FAM19A5 0.762 1.430612 0.262 5 BHLHE22 0.764 1.426500 0.264 5 TUBB2A 0.822 1.419453 0.322 5 VSNL1 0.804 1.414648 0.304 5 STXBP6 0.747 1.412450 0.247 5 PCDH8 0.731 1.408067 0.231 5 TKT 0.843 1.399775 0.343 5 BASP1 0.828 1.397467 0.328 5 EPB4.1L4A 0.763 1.393019 0.263 5 A030009H04RIK 0.803 1.387965 0.303 5 GPM6A 0.841 1.376807 0.341 5 NAP1L5 0.808 1.375097 0.308 5 PCDH17 0.799 1.369359 0.299 5 GABBR2 0.754 1.368149 0.254 5 SYT11 0.845 1.347546 0.345 5 LRRN3 0.721 1.338672 0.221 5 CALB1 0.776 1.334921 0.276 5 SV2A 0.850 1.332636 0.350 5 SCN3A 0.760 1.325687 0.260 5 RYR2 0.782 1.321029 0.282 5 HUNK 0.729 1.315880 0.229 5 BAI3 0.725 1.314119 0.225 5 PCSK2 0.737 1.311312 0.237 5 ADCY2 0.739 1.311003 0.239 5 GNG3 0.799 1.308365 0.299 5 TFAP2A 0.759 1.308229 0.259 5 ZMAT4 0.754 1.305568 0.254 5 FLRT3 0.763 1.304117 0.263 5 GABRA3 0.746 1.300341 0.246 5 DPP6 0.780 1.298661 0.280 5 RASGRF1 0.745 1.298565 0.245 5 SPOCK3 0.705 1.294629 0.205 5 CELF4 0.842 1.286985 0.342 5 SPARCL1 0.778 1.281146 0.278 5 ELAVL4 0.751 1.274854 0.251 5 GRIA4 0.784 1.270207 0.284 5 PKIA 0.775 1.269100 0.275 5 ATRNL1 0.720 1.259867 0.220 5 UCHL1 0.773 1.241952 0.273 5 CRHR2 0.708 1.227419 0.208 5 GRIA2 0.817 1.223394 0.317 5 CACNG3 0.750 1.222476 0.250 5 CDH4 0.729 1.217037 0.229 5 NDRG4 0.774 1.214021 0.274 5 8430419L09RIK 0.718 1.208866 0.218 5 STMN3 0.783 1.205826 0.283 5 NRXN1 0.744 1.199941 0.244 5 DIO2 0.722 1.194141 0.222 5 ANK3 0.796 1.193807 0.296 5 DPYSL4 0.777 1.187574 0.277 5 STMN4 0.747 1.182336 0.247 5 ROBO2 0.705 1.181819 0.205 5 CLMP 0.760 1.181079 0.260 5 UTRN 0.733 1.177432 0.233 5 MLLT11 0.756 1.174966 0.256 5 RELN 0.707 1.172184 0.207 5 STK32B 0.712 1.171383 0.212 5 ATP1A1 0.773 1.171164 0.273 5 TMX4 0.773 1.170468 0.273 5 GAP43 0.739 1.169587 0.239 5 PLCB1 0.709 1.165435 0.209 5 SCN2A1 0.727 1.161847 0.227 5 CDK14 0.755 1.157752 0.255 5 UBASH3B 0.731 1.143693 0.231 5 MYT1L 0.730 1.141047 0.230 5 6330403K07RIK 0.723 1.140026 0.223 5 TTC3 0.833 1.133517 0.333 5 FGF14 0.708 1.123639 0.208 5 NRCAM 0.715 1.121937 0.215 5 LPHN3 0.733 1.121325 0.233 5 NRSN1 0.758 1.116765 0.258 5 BRINP1 0.731 1.116028 0.231 5 DCLK1 0.745 1.111968 0.245 5 SUSD4 0.709 1.111055 0.209 5 4833424O15RIK 0.722 1.108714 0.222 5 CHGA 0.776 1.098459 0.276 5 PBX1 0.777 1.097487 0.277 5 KIF5C 0.747 1.090766 0.247 5 PCP4 0.829 1.082855 0.329 5 SNCA 0.718 1.080615 0.218 5 NCDN 0.740 1.079821 0.240 5 GNAS 0.820 1.079212 0.320 5 CYFIP2 0.764 1.073980 0.264 5 PTPRK 0.702 1.064478 0.202 5 GM1673 0.729 1.060925 0.229 5 HMGCS1 0.753 1.060691 0.253 5 RTN1 0.800 1.055933 0.300 5 IGSF8 0.740 1.055664 0.240 5 SNRPN 0.754 1.038591 0.254 5 THRA 0.772 1.020305 0.272 5 CHD3 0.753 1.009107 0.253 5 GNB1 0.248 −1.603950 0.252 5 HMGN1 0.209 −1.639410 0.291 5 UNC119 0.251 −1.776276 0.249 5 GNAT1 0.224 −1.788295 0.276 5 NEUROD1 0.273 −1.859046 0.227 5 RP1 0.233 −1.902106 0.267 5 PDE6B 0.237 −1.916995 0.263 5 NRL 0.260 −1.922926 0.240 5 RCVRN 0.219 −1.936805 0.281 5 ROM1 0.219 −2.012157 0.281 5 CNGA1 0.253 −2.027682 0.247 5 PDC 0.180 −2.058464 0.320 5 PRPH2 0.189 −2.124104 0.311 5 RHO 0.175 −2.140480 0.325 5 RS1 0.247 −2.154422 0.253 5 SAG 0.166 −2.161915 0.334 5 NR2E3 0.249 −2.164806 0.251 5 GNGT1 0.160 −2.165857 0.340 5 RPGRIP1 0.244 −2.166108 0.256 5 SLC24A1 0.259 −2.174069 0.241 5 TULP1 0.195 −2.237394 0.305 5 PDE6G 0.190 −2.267903 0.310 5 cluster no. 6 DE = 156 NPNT 0.945 2.486780 0.445 6 ARL4C 0.938 2.467107 0.438 6 BHLHE22 0.917 2.421611 0.417 6 CPLX2 0.942 2.362730 0.442 6 LPL 0.920 2.288892 0.420 6 FILIP1L 0.897 2.194008 0.397 6 TKT 0.925 2.156892 0.425 6 NRXN2 0.932 2.155552 0.432 6 SIX3 0.923 2.092244 0.423 6 SLIT2 0.911 2.087468 0.411 6 SNHG11 0.935 2.050363 0.435 6 SLC6A1 0.885 1.911315 0.385 6 PAX6 0.894 1.818176 0.394 6 PTN 0.892 1.811793 0.392 6 RBFOX1 0.853 1.801588 0.353 6 DLGAP1 0.867 1.797541 0.367 6 GRIA2 0.898 1.738590 0.398 6 HBEGF 0.812 1.719168 0.312 6 2900011O08RIK 0.863 1.692404 0.363 6 MEIS2 0.887 1.620756 0.387 6 DTNBP1 0.839 1.601648 0.339 6 GAD1 0.851 1.596819 0.351 6 ATP1B1 0.888 1.593981 0.388 6 ASAP1 0.841 1.587659 0.341 6 FEZ1 0.823 1.583525 0.323 6 SPOCK3 0.826 1.577292 0.326 6 PCDH10 0.841 1.552813 0.341 6 VSNL1 0.819 1.543639 0.319 6 NECAB1 0.807 1.542009 0.307 6 GAD2 0.800 1.511610 0.300 6 NRCAM 0.809 1.495982 0.309 6 GUCY1A3 0.855 1.487265 0.355 6 ID4 0.791 1.477149 0.291 6 BASP1 0.849 1.466807 0.349 6 PDE4B 0.803 1.466115 0.303 6 KCNIP1 0.807 1.464399 0.307 6 CXCL14 0.771 1.455123 0.271 6 KCNC1 0.798 1.426647 0.298 6 RPH3A 0.835 1.420630 0.335 6 FAM155A 0.804 1.420487 0.304 6 UCHL1 0.826 1.419570 0.326 6 DAPK1 0.786 1.411956 0.286 6 TTC3 0.887 1.400846 0.387 6 DPYSL4 0.796 1.396161 0.296 6 GABBR2 0.746 1.395801 0.246 6 CCDC88B 0.779 1.375544 0.279 6 SLC32A1 0.807 1.368830 0.307 6 C1QL1 0.772 1.360801 0.272 6 STMN2 0.812 1.357504 0.312 6 ELAVL3 0.820 1.350815 0.320 6 RND3 0.779 1.347967 0.279 6 GPM6A 0.835 1.344385 0.335 6 MEG3 0.875 1.342623 0.375 6 A030009H04RIK 0.792 1.333141 0.292 6 ZFHX3 0.768 1.332239 0.268 6 RGS7BP 0.769 1.324127 0.269 6 NDRG4 0.822 1.318106 0.322 6 RPS6KA4 0.748 1.311023 0.248 6 ADARB1 0.798 1.302663 0.298 6 FRMD5 0.798 1.291730 0.298 6 TUBB2A 0.825 1.288930 0.325 6 CTNND2 0.771 1.287176 0.271 6 CDK5R1 0.788 1.279842 0.288 6 SV2A 0.826 1.279755 0.326 6 PRKCB 0.782 1.272974 0.282 6 CACNG4 0.807 1.269842 0.307 6 UNC5D 0.741 1.260066 0.241 6 PRMT8 0.753 1.258728 0.253 6 CACNA2D1 0.769 1.257272 0.269 6 GNG3 0.817 1.251172 0.317 6 AUTS2 0.781 1.247146 0.281 6 STMN3 0.820 1.245952 0.320 6 FAIM2 0.772 1.244633 0.272 6 PNMAL2 0.804 1.239124 0.304 6 UBASH3B 0.720 1.237485 0.220 6 RUNX1T1 0.768 1.222632 0.268 6 LRP8 0.761 1.212309 0.261 6 STMN1 0.775 1.209730 0.275 6 6430548M08RIK 0.803 1.207834 0.303 6 MPP6 0.761 1.206435 0.261 6 GPR123 0.736 1.204882 0.236 6 LHFPL2 0.719 1.202920 0.219 6 COL6A1 0.747 1.199489 0.247 6 DHCR24 0.745 1.195008 0.245 6 DUSP26 0.791 1.193817 0.291 6 ALCAM 0.712 1.183433 0.212 6 INPP4B 0.736 1.177319 0.236 6 CLMN 0.701 1.175226 0.201 6 TSC22D1 0.819 1.174524 0.319 6 SNRPN 0.792 1.174384 0.292 6 CELF4 0.835 1.173654 0.335 6 HUNK 0.737 1.169421 0.237 6 TNC 0.723 1.167862 0.223 6 TFAP2A 0.734 1.161882 0.234 6 RASAL2 0.740 1.156727 0.240 6 FGD6 0.741 1.156173 0.241 6 ELAVL4 0.762 1.149500 0.262 6 GNG2 0.760 1.147975 0.260 6 LPHN3 0.713 1.131097 0.213 6 PLCH1 0.734 1.129860 0.234 6 PCDH17 0.730 1.127561 0.230 6 AI848285 0.704 1.120084 0.204 6 MYH10 0.779 1.111490 0.279 6 TMEM191C 0.740 1.110693 0.240 6 GRIA4 0.752 1.109848 0.252 6 THRA 0.801 1.109794 0.301 6 RASGRF1 0.710 1.104095 0.210 6 CHN1 0.759 1.098900 0.259 6 CDC42EP4 0.706 1.091060 0.206 6 KIF5C 0.779 1.081707 0.279 6 GAS7 0.763 1.080142 0.263 6 FSCN1 0.753 1.069197 0.253 6 6330403K07RIK 0.713 1.065402 0.213 6 TAGLN3 0.766 1.056235 0.266 6 BC048943 0.768 1.055497 0.268 6 ATP6V1G2 0.749 1.049524 0.249 6 GABRA3 0.738 1.046500 0.238 6 HPCA 0.749 1.045573 0.249 6 FUT9 0.706 1.043984 0.206 6 CERS5 0.745 1.040396 0.245 6 FAM115A 0.777 1.038889 0.277 6 SFXN1 0.726 1.037528 0.226 6 MLLT11 0.773 1.035476 0.273 6 SYNPR 0.758 1.032318 0.258 6 CX3CL1 0.708 1.025068 0.208 6 MAPT 0.773 1.017509 0.273 6 DAAM1 0.744 1.012920 0.244 6 CMIP 0.752 1.011512 0.252 6 DKK3 0.836 1.011427 0.336 6 IGSF8 0.733 1.003250 0.233 6 TENM4 0.703 1.002356 0.203 6 NSG2 0.752 1.001377 0.252 6 NRSN1 0.747 1.000763 0.247 6 CST3 0.293 −1.465866 0.207 6 UNC119 0.276 −1.522563 0.224 6 HMGN1 0.218 −1.541634 0.282 6 ROM1 0.257 −1.544670 0.243 6 GNB1 0.254 −1.581356 0.246 6 RPGRIP1 0.279 −1.586358 0.221 6 NEUROD1 0.296 −1.619679 0.204 6 NRL 0.281 −1.643732 0.219 6 CNGA1 0.281 −1.691412 0.219 6 PRPH2 0.220 −1.692216 0.280 6 TULP1 0.227 −1.729834 0.273 6 NR2E3 0.278 −1.736613 0.222 6 RP1 0.256 −1.749063 0.244 6 RS1 0.278 −1.760521 0.222 6 PDE6B 0.253 −1.770264 0.247 6 PDE6G 0.227 −1.826063 0.273 6 SLC24A1 0.290 −1.831021 0.210 6 SAG 0.180 −1.853215 0.320 6 RCVRN 0.234 −1.864629 0.266 6 GNAT1 0.222 −1.882724 0.278 6 GNGT1 0.190 −1.891447 0.310 6 RHO 0.184 −1.906823 0.316 6 PDC 0.188 −1.952769 0.312 6 cluster no. 7 DE = 164 CXCL14 0.953 2.823229 0.453 7 CPLX2 0.965 2.782527 0.465 7 MAF 0.874 2.663386 0.374 7 AI593442 0.929 2.533839 0.429 7 ID4 0.900 2.369125 0.400 7 LPL 0.929 2.294283 0.429 7 GAD2 0.909 2.222806 0.409 7 NPNT 0.872 2.100390 0.372 7 SNHG11 0.933 2.095661 0.433 7 SPOCK3 0.907 2.024941 0.407 7 PAX6 0.906 1.900148 0.406 7 NRXN2 0.889 1.824692 0.389 7 GRIA2 0.907 1.794039 0.407 7 NDRG4 0.889 1.706384 0.389 7 2900011O08RIK 0.866 1.702616 0.366 7 DTNBP1 0.860 1.674204 0.360 7 C1QL1 0.836 1.656812 0.336 7 ASAP1 0.848 1.646246 0.348 7 ATP1B1 0.904 1.636111 0.404 7 SIX3 0.866 1.635263 0.366 7 SLC6A1 0.852 1.618210 0.352 7 FILIP1L 0.801 1.610463 0.301 7 HBEGF 0.809 1.597965 0.309 7 PDE4B 0.838 1.597787 0.338 7 GUCY1A3 0.864 1.582330 0.364 7 GAD1 0.851 1.579238 0.351 7 TNC 0.793 1.575202 0.293 7 CRYBB3 0.732 1.574911 0.232 7 ADARB1 0.842 1.560392 0.342 7 MMP9 0.744 1.559409 0.244 7 DNER 0.836 1.558484 0.336 7 SPARCL1 0.843 1.550294 0.343 7 DDAH1 0.829 1.541302 0.329 7 DLGAP1 0.827 1.529146 0.327 7 UACA 0.780 1.515731 0.280 7 MEIS2 0.864 1.513207 0.364 7 RBFOX1 0.805 1.507393 0.305 7 TKT 0.856 1.505182 0.356 7 PCDH7 0.764 1.500815 0.264 7 BHLHE22 0.799 1.499124 0.299 7 CLMN 0.781 1.470727 0.281 7 SLC32A1 0.819 1.466542 0.319 7 BASP1 0.846 1.464820 0.346 7 ELMO1 0.787 1.457100 0.287 7 CACNG4 0.843 1.450213 0.343 7 TUBB2A 0.849 1.445571 0.349 7 GNG2 0.827 1.438293 0.327 7 GNG3 0.863 1.436732 0.363 7 DKK3 0.893 1.435250 0.393 7 KCNA6 0.749 1.425320 0.249 7 NECAB1 0.792 1.419522 0.292 7 KCNAB1 0.799 1.416904 0.299 7 ALDOC 0.847 1.409296 0.347 7 LMO4 0.743 1.409237 0.243 7 6430548M08RIK 0.830 1.391242 0.330 7 FAM155A 0.818 1.381329 0.318 7 PNMAL2 0.836 1.374099 0.336 7 KCNC1 0.817 1.373826 0.317 7 ARL4C 0.783 1.370410 0.283 7 SCN3A 0.775 1.364878 0.275 7 SYT7 0.778 1.363974 0.278 7 KIF5C 0.828 1.361801 0.328 7 TFAP2C 0.770 1.353114 0.270 7 FEZ1 0.792 1.342916 0.292 7 PTN 0.817 1.337424 0.317 7 CELF4 0.871 1.326774 0.371 7 TTC3 0.867 1.304565 0.367 7 CPNE6 0.779 1.303567 0.279 7 SV2A 0.842 1.297245 0.342 7 CTSL 0.838 1.288834 0.338 7 MYH10 0.809 1.279803 0.309 7 GABBR2 0.754 1.279715 0.254 7 FRMD5 0.810 1.278237 0.310 7 PAK3 0.798 1.275646 0.298 7 PRKCB 0.771 1.274230 0.271 7 ELAVL3 0.811 1.271094 0.311 7 ADARB2 0.740 1.270445 0.240 7 ARHGEF9 0.788 1.265682 0.288 7 HUNK 0.784 1.259809 0.284 7 OGFRL1 0.809 1.255789 0.309 7 CPNE5 0.746 1.249717 0.246 7 THRA 0.835 1.245177 0.335 7 KCNA1 0.753 1.239065 0.253 7 KCNIP1 0.761 1.237502 0.261 7 SLIT2 0.767 1.237248 0.267 7 DPYSL4 0.786 1.232672 0.286 7 C1QL2 0.751 1.228475 0.251 7 THY1 0.774 1.227368 0.274 7 PRUNE2 0.760 1.221889 0.260 7 ALCAM 0.713 1.207316 0.213 7 DHCR24 0.776 1.189385 0.276 7 STMN3 0.826 1.187067 0.326 7 CD302 0.706 1.182568 0.206 7 PRRT4 0.736 1.180616 0.236 7 PCSK2 0.749 1.162396 0.249 7 DAPK1 0.751 1.145777 0.251 7 SEZ6L 0.717 1.139465 0.217 7 SFXN1 0.757 1.133703 0.257 7 SYNPR 0.800 1.123498 0.300 7 VPS41 0.772 1.123170 0.272 7 NSG2 0.798 1.120197 0.298 7 CCDC88B 0.730 1.113578 0.230 7 STMN2 0.785 1.107607 0.285 7 MLLT11 0.797 1.107556 0.297 7 A030009H04RIK 0.780 1.107031 0.280 7 VSNL1 0.752 1.103676 0.252 7 TAGLN3 0.802 1.102230 0.302 7 ELAVL4 0.743 1.101000 0.243 7 LHFPL2 0.715 1.100922 0.215 7 FRRS1L 0.713 1.100870 0.213 7 CERS5 0.750 1.098213 0.250 7 RND3 0.726 1.095740 0.226 7 SNRPN 0.789 1.095115 0.289 7 GABRA3 0.732 1.090441 0.232 7 PPFIBP1 0.706 1.080047 0.206 7 GAS7 0.770 1.079250 0.270 7 INPP4B 0.710 1.078757 0.210 7 ATP6V1D 0.816 1.078276 0.316 7 FGD6 0.721 1.073414 0.221 7 SPAG5 0.701 1.072308 0.201 7 ATP6V1G2 0.767 1.068453 0.267 7 HPCA 0.758 1.064513 0.258 7 ARHGAP24 0.776 1.063113 0.276 7 UBASH3B 0.720 1.061516 0.220 7 NAP1L5 0.804 1.060330 0.304 7 CACNG3 0.726 1.057606 0.226 7 MXRA7 0.781 1.057379 0.281 7 ADCY2 0.733 1.055474 0.233 7 SYT11 0.792 1.054558 0.292 7 NPTX2 0.712 1.054379 0.212 7 RPS6KA4 0.712 1.051053 0.212 7 UTRN 0.714 1.049219 0.214 7 BC048943 0.793 1.047734 0.293 7 LPHN3 0.745 1.043811 0.245 7 MAPT 0.764 1.036973 0.264 7 CTNND2 0.734 1.032653 0.234 7 AUTS2 0.749 1.032249 0.249 7 SEPT11 0.742 1.032183 0.242 7 DAAM1 0.774 1.031598 0.274 7 PCP4L1 0.801 1.029783 0.301 7 CACNB4 0.709 1.012396 0.209 7 MPP6 0.713 1.012265 0.213 7 MARCKS 0.788 1.005504 0.288 7 GNB1 0.279 −1.346821 0.221 7 CST3 0.298 −1.460907 0.202 7 HMGN1 0.231 −1.484621 0.269 7 ROM1 0.265 −1.549354 0.235 7 NEUROD1 0.297 −1.576758 0.203 7 CNGA1 0.290 −1.593189 0.210 7 RPGRIP1 0.294 −1.594350 0.206 7 RP1 0.270 −1.600478 0.230 7 TULP1 0.236 −1.643426 0.264 7 NRL 0.284 −1.667221 0.216 7 PDE6G 0.239 −1.675754 0.261 7 RCVRN 0.248 −1.702941 0.252 7 PDE6B 0.252 −1.720612 0.248 7 SLC24A1 0.296 −1.738209 0.204 7 GNGT1 0.202 −1.745068 0.298 7 PRPH2 0.215 −1.759684 0.285 7 RS1 0.283 −1.786494 0.217 7 GNAT1 0.231 −1.791925 0.269 7 PDC 0.196 −1.850697 0.304 7 SAG 0.181 −1.888991 0.319 7 RHO 0.184 −1.898958 0.316 7 cluster no. 8 DE = 145 TAC2 0.842 3.118377 0.342 8 TAC1 0.795 2.770889 0.295 8 STMN2 0.906 2.529338 0.406 8 GAP43 0.840 2.159206 0.340 8 NAP1L5 0.913 2.129231 0.413 8 ATP1B1 0.916 2.091522 0.416 8 C1QL1 0.847 2.085033 0.347 8 CXCL14 0.750 2.075283 0.250 8 MEG3 0.891 2.041950 0.391 8 SNHG11 0.891 2.017042 0.391 8 6330403K07RIK 0.824 1.993265 0.324 8 2900011O08RIK 0.866 1.975900 0.366 8 UCHL1 0.861 1.947723 0.361 8 ELAVL2 0.762 1.912945 0.262 8 STMN3 0.861 1.816792 0.361 8 CBLN2 0.711 1.800970 0.211 8 SPOCK3 0.809 1.777251 0.309 8 NCAM2 0.788 1.760593 0.288 8 TUBB2A 0.861 1.711072 0.361 8 TFAP2B 0.808 1.701796 0.308 8 SNCA 0.784 1.697970 0.284 8 SLC32A1 0.809 1.663189 0.309 8 SCG2 0.824 1.651755 0.324 8 STMN4 0.809 1.645436 0.309 8 CPNE5 0.789 1.634672 0.289 8 RTN1 0.852 1.593975 0.352 8 VSNL1 0.806 1.570547 0.306 8 IMPACT 0.827 1.556732 0.327 8 SORCS1 0.773 1.555793 0.273 8 GAD2 0.778 1.543560 0.278 8 BASP1 0.832 1.538785 0.332 8 CPLX2 0.799 1.530079 0.299 8 MEIS2 0.819 1.506350 0.319 8 GNG2 0.785 1.503115 0.285 8 OXR1 0.753 1.492338 0.253 8 GNG3 0.817 1.478047 0.317 8 CELF4 0.859 1.464056 0.359 8 DNER 0.783 1.449112 0.283 8 TTC3 0.889 1.424931 0.389 8 LSAMP 0.793 1.418734 0.293 8 NRXN2 0.815 1.414066 0.315 8 YWHAH 0.794 1.410703 0.294 8 NECAB1 0.765 1.405108 0.265 8 SERPINE2 0.701 1.401238 0.201 8 A030009H04RIK 0.788 1.397833 0.288 8 ZWINT 0.808 1.388499 0.308 8 SLC6A1 0.797 1.385051 0.297 8 SYT11 0.816 1.379877 0.316 8 GPRASP1 0.799 1.359056 0.299 8 4833424O15RIK 0.751 1.355348 0.251 8 AI593442 0.713 1.339073 0.213 8 C1QL2 0.722 1.335015 0.222 8 MLLT11 0.782 1.331374 0.282 8 GRIA2 0.831 1.326882 0.331 8 MARCKS 0.827 1.322535 0.327 8 SYT6 0.714 1.321108 0.214 8 NRSN1 0.757 1.319183 0.257 8 TFAP2A 0.713 1.313087 0.213 8 KIF5C 0.784 1.312896 0.284 8 SYN2 0.711 1.309829 0.211 8 TENM1 0.730 1.298569 0.230 8 EPB4.1L4A 0.713 1.296585 0.213 8 PAX6 0.784 1.290939 0.284 8 NDN 0.805 1.284206 0.305 8 GPM6A 0.815 1.282328 0.315 8 FXYD6 0.740 1.268025 0.240 8 GNAS 0.849 1.267517 0.349 8 SYT7 0.738 1.267166 0.238 8 SNRPN 0.765 1.264829 0.265 8 SPOCK2 0.771 1.263568 0.271 8 PNMAL2 0.771 1.259210 0.271 8 MAPT 0.770 1.244983 0.270 8 MYT1L 0.752 1.241489 0.252 8 HSP90AB1 0.873 1.236035 0.373 8 BEX2 0.803 1.234094 0.303 8 NDRG4 0.787 1.229175 0.287 8 TKT 0.747 1.227076 0.247 8 GAD1 0.734 1.218844 0.234 8 TCEAL5 0.748 1.204026 0.248 8 TENM4 0.719 1.203590 0.219 8 NSG2 0.743 1.203252 0.243 8 SYNGR3 0.735 1.190339 0.235 8 YWHAG 0.770 1.184824 0.270 8 GRIA3 0.714 1.181042 0.214 8 FSTL5 0.737 1.177141 0.237 8 NSG1 0.744 1.176358 0.244 8 SPARCL1 0.762 1.174529 0.262 8 TMX4 0.753 1.169642 0.253 8 REEP5 0.734 1.166393 0.234 8 SYNPR 0.769 1.155357 0.269 8 TUBA1A 0.793 1.148357 0.293 8 NGFRAP1 0.765 1.133358 0.265 8 TMSB10 0.770 1.132117 0.270 8 CACNA2D2 0.723 1.117231 0.223 8 CALM2 0.801 1.114501 0.301 8 RBFOX1 0.712 1.114299 0.212 8 PRKAR1B 0.731 1.103338 0.231 8 GM1673 0.720 1.098747 0.220 8 SERINC1 0.791 1.098697 0.291 8 SV2A 0.782 1.095818 0.282 8 APP 0.748 1.089714 0.248 8 ZCCHC18 0.740 1.081228 0.240 8 CALM3 0.773 1.071503 0.273 8 GPRASP2 0.738 1.069399 0.238 8 RAB6B 0.736 1.066369 0.236 8 GRIA4 0.729 1.065619 0.229 8 LRRC4C 0.701 1.059002 0.201 8 KIF5A 0.709 1.050514 0.209 8 DTNBP1 0.720 1.042708 0.220 8 RAB6A 0.752 1.040196 0.252 8 CD200 0.709 1.038274 0.209 8 CHGA 0.752 1.036109 0.252 8 KIF3A 0.743 1.036025 0.243 8 CDK5R1 0.735 1.035358 0.235 8 ACOT7 0.715 1.035198 0.215 8 CACNG4 0.739 1.032652 0.239 8 TPM1 0.732 1.026026 0.232 8 OLFM1 0.705 1.021312 0.205 8 ELAVL3 0.744 1.019574 0.244 8 KIFAP3 0.762 1.019268 0.262 8 D3BWG0562E 0.717 1.014043 0.217 8 EPB4.1 0.292 −1.765515 0.208 8 GNB1 0.237 −1.768119 0.263 8 NEUROD1 0.267 −1.803015 0.233 8 HMGN1 0.185 −1.892020 0.315 8 AIPL1 0.297 −1.980059 0.203 8 UNC119 0.225 −2.011676 0.275 8 RP1 0.218 −2.092140 0.282 8 NR2E3 0.243 −2.160752 0.257 8 NRL 0.239 −2.167337 0.261 8 CNGA1 0.229 −2.233635 0.271 8 ROM1 0.197 −2.307381 0.303 8 PDE6B 0.210 −2.319014 0.290 8 RPGRIP1 0.231 −2.350954 0.269 8 PRPH2 0.170 −2.376545 0.330 8 PDE6G 0.187 −2.377062 0.313 8 RS1 0.230 −2.386965 0.270 8 SLC24A1 0.240 −2.450802 0.260 8 GNAT1 0.176 −2.480741 0.324 8 SAG 0.140 −2.481892 0.360 8 RCVRN 0.187 −2.497213 0.313 8 RHO 0.146 −2.536232 0.354 8 GNGT1 0.133 −2.654791 0.367 8 TULP1 0.165 −2.680406 0.335 8 PDC 0.144 −2.702042 0.356 8 cluster no. 9 DE = 145 TFAP2B 0.913 2.692482 0.413 9 ATP1B1 0.940 2.501021 0.440 9 C1QL1 0.921 2.473758 0.421 9 CBLN2 0.903 2.412823 0.403 9 MARCKS 0.932 2.121128 0.432 9 SNHG11 0.938 2.107667 0.438 9 OLFM3 0.817 2.099649 0.317 9 FILIP1L 0.824 2.028323 0.324 9 SLC6A1 0.888 1.981368 0.388 9 NRXN2 0.882 1.930215 0.382 9 GAD1 0.888 1.921463 0.388 9 CACNA2D2 0.851 1.807684 0.351 9 CHGA 0.879 1.793344 0.379 9 C1QL2 0.838 1.774575 0.338 9 BASP1 0.866 1.743280 0.366 9 GAP43 0.818 1.741537 0.318 9 IGFBP2 0.769 1.726564 0.269 9 TBX3 0.788 1.699690 0.288 9 TFAP2A 0.806 1.692081 0.306 9 SYT7 0.773 1.670252 0.273 9 LRRN3 0.806 1.657383 0.306 9 ADARB1 0.839 1.646173 0.339 9 UCHL1 0.864 1.644685 0.364 9 PAX6 0.862 1.638791 0.362 9 MEG3 0.907 1.603859 0.407 9 DTNBP1 0.818 1.591595 0.318 9 6430548M08RIK 0.838 1.587475 0.338 9 ELAVL3 0.838 1.578197 0.338 9 KCNAB1 0.817 1.568133 0.317 9 GNG2 0.797 1.564002 0.297 9 NPTX2 0.756 1.555687 0.256 9 AI593442 0.796 1.544986 0.296 9 CELF4 0.886 1.538491 0.386 9 FRMD5 0.828 1.522471 0.328 9 EEF1E1 0.810 1.514397 0.310 9 WBSCR17 0.788 1.491530 0.288 9 PDE3A 0.761 1.485254 0.261 9 RGS8 0.793 1.484142 0.293 9 ELOVL6 0.784 1.477738 0.284 9 MEIS2 0.840 1.475665 0.340 9 GNG3 0.848 1.474628 0.348 9 SLC32A1 0.815 1.466099 0.315 9 ID4 0.756 1.412435 0.256 9 SYNPR 0.826 1.410647 0.326 9 PRKAR2B 0.775 1.395009 0.275 9 LIN7A 0.849 1.394313 0.349 9 MAPT 0.808 1.376944 0.308 9 GABRA3 0.779 1.365007 0.279 9 RYR2 0.766 1.360686 0.266 9 NDRG4 0.822 1.358598 0.322 9 PRKCE 0.808 1.355495 0.308 9 LOXL2 0.729 1.349178 0.229 9 ATP2B4 0.739 1.348942 0.239 9 NETO2 0.745 1.311499 0.245 9 ALDOC 0.789 1.306160 0.289 9 WDR1 0.781 1.305461 0.281 9 GRIA3 0.760 1.295841 0.260 9 PHACTR3 0.773 1.289285 0.273 9 FABP3 0.744 1.276022 0.244 9 TUBB2A 0.815 1.274976 0.315 9 LSAMP 0.793 1.272650 0.293 9 SLC6A11 0.755 1.267563 0.255 9 DLGAP1 0.769 1.263219 0.269 9 NAV1 0.788 1.259667 0.288 9 CPNE6 0.754 1.258456 0.254 9 TMEM191C 0.749 1.258008 0.249 9 SOX5 0.723 1.251003 0.223 9 CPLX3 0.800 1.243626 0.300 9 BC048943 0.813 1.230504 0.313 9 SEMA6A 0.748 1.229853 0.248 9 CCDC88B 0.726 1.229532 0.226 9 STMN3 0.814 1.228264 0.314 9 CLMP 0.711 1.227889 0.211 9 HABP4 0.785 1.219840 0.285 9 KIF5C 0.805 1.219184 0.305 9 MARCKSL1 0.788 1.217008 0.288 9 VSNL1 0.757 1.216340 0.257 9 LHX9 0.721 1.197847 0.221 9 GABRG2 0.752 1.191982 0.252 9 ARHGAP20 0.723 1.191230 0.223 9 KCNA1 0.724 1.188659 0.224 9 ATP2B1 0.807 1.184449 0.307 9 TPM1 0.770 1.181575 0.270 9 SV2A 0.806 1.181132 0.306 9 NSG1 0.782 1.178500 0.282 9 TTC3 0.842 1.174244 0.342 9 NAP1L5 0.796 1.164618 0.296 9 A030009H04RIK 0.780 1.164223 0.280 9 DPYSL2 0.770 1.157861 0.270 9 THY1 0.740 1.146696 0.240 9 GPRASP1 0.810 1.145215 0.310 9 SPOCK3 0.767 1.142882 0.267 9 MLLT11 0.780 1.141012 0.280 9 RTN1 0.809 1.140966 0.309 9 CHD3 0.776 1.135535 0.276 9 HSD17B12 0.790 1.135531 0.290 9 RUNX1T1 0.759 1.130153 0.259 9 ITM2C 0.799 1.124268 0.299 9 HSP90AB1 0.842 1.112076 0.342 9 SRGAP3 0.742 1.110121 0.242 9 GNAS 0.841 1.102581 0.341 9 CHGB 0.803 1.091361 0.303 9 NSG2 0.757 1.091212 0.257 9 OXR1 0.757 1.084787 0.257 9 SYT11 0.778 1.081871 0.278 9 CYFIP2 0.748 1.077168 0.248 9 ZEB2 0.742 1.075057 0.242 9 DPP6 0.743 1.072735 0.243 9 CD47 0.784 1.071126 0.284 9 IMPACT 0.764 1.070542 0.264 9 HSPA12A 0.755 1.068676 0.255 9 SH3BP5 0.716 1.067537 0.216 9 RBFOX2 0.741 1.063177 0.241 9 TPPP 0.713 1.062940 0.213 9 SNCB 0.789 1.062761 0.289 9 COL23A1 0.760 1.056620 0.260 9 CALM3 0.777 1.053213 0.277 9 TKT 0.775 1.051475 0.275 9 EPB4.1L4A 0.701 1.043584 0.201 9 FBXO32 0.705 1.032729 0.205 9 GM1673 0.732 1.019184 0.232 9 FAM115A 0.749 1.016772 0.249 9 ECE1 0.704 1.007563 0.204 9 YWHAG 0.760 1.007454 0.260 9 GNB1 0.254 −1.559334 0.246 9 HMGN1 0.215 −1.594457 0.285 9 RP1 0.248 −1.629751 0.252 9 UNC119 0.267 −1.642090 0.233 9 NR2E3 0.271 −1.829500 0.229 9 CNGA1 0.268 −1.870963 0.232 9 TULP1 0.211 −1.901678 0.289 9 ROM1 0.231 −1.909354 0.269 9 RPGRIP1 0.259 −1.928781 0.241 9 NRL 0.257 −1.975762 0.243 9 PRPH2 0.194 −1.981809 0.306 9 SLC24A1 0.266 −1.993990 0.234 9 PDE6G 0.205 −2.045103 0.295 9 RS1 0.259 −2.057027 0.241 9 PDE6B 0.227 −2.071134 0.273 9 RCVRN 0.215 −2.076463 0.285 9 GNAT1 0.204 −2.091716 0.296 9 SAG 0.157 −2.182196 0.343 9 PDC 0.170 −2.185807 0.330 9 RHO 0.163 −2.201967 0.337 9 GNGT1 0.163 −2.222527 0.337 9 cluster no. 10 DE = 120 VIP 0.767 3.830134 0.267 10 CARTPT 0.830 2.551837 0.330 10 CBLN2 0.897 2.371861 0.397 10 SLC6A1 0.912 2.250550 0.412 10 GABRA2 0.841 2.143980 0.341 10 SNHG11 0.945 2.134197 0.445 10 NR4A2 0.835 2.098562 0.335 10 NNAT 0.800 2.051593 0.300 10 CBLN4 0.727 2.045730 0.227 10 TFAP2B 0.876 2.024379 0.376 10 GAD1 0.855 1.986823 0.355 10 6430548M08RIK 0.876 1.940600 0.376 10 NAP1L5 0.892 1.812106 0.392 10 NRSN1 0.822 1.779217 0.322 10 GRIA3 0.750 1.767426 0.250 10 MEG3 0.912 1.766291 0.412 10 SYT6 0.739 1.722186 0.239 10 GAD2 0.795 1.711410 0.295 10 CELF4 0.909 1.695323 0.409 10 2900011O08RIK 0.847 1.663963 0.347 10 STMN4 0.794 1.657861 0.294 10 ATP1B1 0.885 1.613084 0.385 10 RAB3C 0.824 1.612804 0.324 10 CACNA2D2 0.800 1.543215 0.300 10 TKT 0.827 1.542467 0.327 10 MARCKS 0.861 1.534529 0.361 10 RNF220 0.820 1.519204 0.320 10 PAX6 0.826 1.494666 0.326 10 GAP43 0.736 1.494533 0.236 10 ELAVL3 0.829 1.476012 0.329 10 LRRTM1 0.745 1.466343 0.245 10 4833424O15RIK 0.735 1.455809 0.235 10 NDRG4 0.835 1.451943 0.335 10 SLC32A1 0.824 1.449471 0.324 10 HS6ST2 0.717 1.430399 0.217 10 SYT1 0.914 1.419385 0.414 10 GNG2 0.765 1.399822 0.265 10 ZCCHC12 0.711 1.393990 0.211 10 UCHL1 0.797 1.376379 0.297 10 HLF 0.815 1.374388 0.315 10 VSNL1 0.745 1.358259 0.245 10 GNG3 0.826 1.316743 0.326 10 A030009H04RIK 0.787 1.309649 0.287 10 TTC3 0.866 1.305950 0.366 10 BASP1 0.796 1.302431 0.296 10 GPM6A 0.847 1.301211 0.347 10 SYNPR 0.808 1.298962 0.308 10 TAGLN3 0.796 1.289770 0.296 10 DLGAP1 0.744 1.260355 0.244 10 GPRASP1 0.802 1.252102 0.302 10 SLC6A11 0.734 1.251426 0.234 10 KIF5C 0.790 1.248227 0.290 10 NDN 0.799 1.209617 0.299 10 ELAVL4 0.715 1.195958 0.215 10 GABRG2 0.766 1.191704 0.266 10 NSG2 0.748 1.180941 0.248 10 RUNX1T1 0.711 1.177126 0.211 10 PNMAL2 0.766 1.175260 0.266 10 NSG1 0.771 1.173586 0.271 10 CHD5 0.711 1.168834 0.211 10 SV2A 0.812 1.167811 0.312 10 GABRA3 0.702 1.163683 0.202 10 BEX1 0.754 1.160868 0.254 10 GRM1 0.704 1.158057 0.204 10 NGFRAP1 0.793 1.157896 0.293 10 SPOCK3 0.730 1.139949 0.230 10 6330403K07RIK 0.723 1.136823 0.223 10 IMPACT 0.756 1.136763 0.256 10 GRIA4 0.705 1.134038 0.205 10 STMN2 0.719 1.126131 0.219 10 MAPT 0.773 1.125378 0.273 10 MARCKSL1 0.764 1.124830 0.264 10 PAK3 0.730 1.118891 0.230 10 ZCCHC18 0.765 1.116777 0.265 10 CACNG3 0.702 1.116442 0.202 10 GRIA2 0.788 1.114119 0.288 10 YWHAH 0.753 1.111748 0.253 10 SYT4 0.745 1.111249 0.245 10 TCEAL5 0.733 1.104752 0.233 10 SYT11 0.783 1.101958 0.283 10 STMN3 0.750 1.099315 0.250 10 NRXN2 0.768 1.098278 0.268 10 SLC22A17 0.754 1.090749 0.254 10 LY6H 0.721 1.080063 0.221 10 FXYD6 0.727 1.064334 0.227 10 FAM115A 0.734 1.055395 0.234 10 GM1673 0.723 1.055283 0.223 10 GNAS 0.822 1.047020 0.322 10 APP 0.755 1.039216 0.255 10 CACNG4 0.730 1.037850 0.230 10 ZWINT 0.752 1.036807 0.252 10 TMEM130 0.701 1.032886 0.201 10 D3BWG0562E 0.716 1.025310 0.216 10 LIN7A 0.775 1.021321 0.275 10 MLLT11 0.750 1.017950 0.250 10 RTN1 0.801 1.016598 0.301 10 BEX2 0.797 1.008599 0.297 10 SNRPN 0.753 1.000211 0.253 10 GNB1 0.252 −1.543483 0.248 10 HMGN1 0.216 −1.579673 0.284 10 CNGA1 0.274 −1.673428 0.226 10 UNC119 0.245 −1.746828 0.255 10 NRL 0.262 −1.796001 0.238 10 NEUROD1 0.277 −1.806110 0.223 10 NR2E3 0.256 −1.883207 0.244 10 PDE6B 0.229 −1.927154 0.271 10 ROM1 0.218 −1.942172 0.282 10 RP1 0.231 −1.972704 0.269 10 TULP1 0.205 −1.993368 0.295 10 PRPH2 0.192 −2.009075 0.308 10 RCVRN 0.217 −2.034673 0.283 10 PDE6G 0.197 −2.035379 0.303 10 GNAT1 0.205 −2.035699 0.295 10 SLC24A1 0.258 −2.054582 0.242 10 GNGT1 0.164 −2.075342 0.336 10 RS1 0.255 −2.087538 0.245 10 RPGRIP1 0.240 −2.097159 0.260 10 SAG 0.155 −2.153542 0.345 10 PDC 0.169 −2.178552 0.331 10 RHO 0.160 −2.190204 0.340 10 cluster no. 11 DE = 111 SLC6A1 0.931 2.333915 0.431 11 PCDH17 0.863 2.136196 0.363 11 DNER 0.885 2.116049 0.385 11 ID4 0.806 2.095898 0.306 11 TFAP2B 0.830 2.083132 0.330 11 SNHG11 0.930 2.057025 0.430 11 SYT7 0.813 2.030645 0.313 11 ATP1B1 0.914 1.999429 0.414 11 GAD1 0.851 1.909032 0.351 11 MEIS2 0.877 1.853622 0.377 11 SYNPR 0.879 1.830302 0.379 11 SPARCL1 0.787 1.809269 0.287 11 FRMD5 0.838 1.786740 0.338 11 TKT 0.863 1.751565 0.363 11 GRIA2 0.861 1.721134 0.361 11 AI848285 0.734 1.720216 0.234 11 GFRA1 0.753 1.715834 0.253 11 MEG3 0.905 1.705098 0.405 11 NDRG4 0.850 1.687578 0.350 11 NAP1L5 0.848 1.685433 0.348 11 PAX6 0.822 1.680286 0.322 11 ESRRG 0.754 1.614605 0.254 11 PTPRT 0.714 1.601504 0.214 11 NRXN2 0.825 1.588975 0.325 11 6430548M08RIK 0.813 1.574957 0.313 11 ADARB1 0.801 1.564237 0.301 11 ELAVL3 0.828 1.553803 0.328 11 BASP1 0.839 1.545173 0.339 11 GAD2 0.764 1.519852 0.264 11 ZFHX3 0.783 1.488418 0.283 11 GABRG2 0.811 1.485814 0.311 11 CACNA2D2 0.763 1.479819 0.263 11 VSNL1 0.757 1.475157 0.257 11 SV2A 0.838 1.462079 0.338 11 CELF4 0.867 1.458085 0.367 11 DPP6 0.778 1.451701 0.278 11 DUSP26 0.785 1.449344 0.285 11 CHN2 0.719 1.444832 0.219 11 TSHZ1 0.701 1.403224 0.201 11 DYNC1I1 0.719 1.398013 0.219 11 DLGAP1 0.763 1.388125 0.263 11 SLC32A1 0.776 1.339618 0.276 11 APP 0.827 1.335361 0.327 11 VSTM2B 0.708 1.333834 0.208 11 2900011O08RIK 0.788 1.318652 0.288 11 LDHB 0.766 1.315407 0.266 11 SPOCK3 0.772 1.315060 0.272 11 TTC3 0.855 1.308993 0.355 11 ELAVL4 0.723 1.307010 0.223 11 CYGB 0.743 1.300364 0.243 11 NRSN1 0.756 1.299498 0.256 11 GNG3 0.804 1.280594 0.304 11 NRXN1 0.725 1.273732 0.225 11 KIF5C 0.766 1.262018 0.266 11 TMEM191C 0.728 1.250965 0.228 11 RIT2 0.737 1.246639 0.237 11 PCP4 0.706 1.237709 0.206 11 RGS8 0.709 1.234002 0.209 11 PNMAL2 0.770 1.228431 0.270 11 STMN3 0.807 1.225751 0.307 11 FABP3 0.704 1.222551 0.204 11 CALY 0.729 1.220655 0.229 11 CHN1 0.749 1.219803 0.249 11 A030009H04RIK 0.740 1.205040 0.240 11 SIX6 0.711 1.201685 0.211 11 DKK3 0.804 1.196969 0.304 11 GPRASP1 0.788 1.175368 0.288 11 TMX4 0.746 1.167458 0.246 11 DHCR24 0.702 1.159663 0.202 11 SYT11 0.750 1.142552 0.250 11 NSG2 0.709 1.124489 0.209 11 RPH3A 0.713 1.118261 0.213 11 AUTS2 0.710 1.102486 0.210 11 GPM6A 0.778 1.101162 0.278 11 CYFIP2 0.731 1.094488 0.231 11 CD47 0.738 1.094214 0.238 11 GRIA4 0.709 1.066388 0.209 11 PBX1 0.760 1.064081 0.260 11 PRKACB 0.721 1.048412 0.221 11 SYT4 0.708 1.043194 0.208 11 MAPT 0.729 1.037623 0.229 11 SERINC1 0.789 1.037343 0.289 11 GABRA1 0.728 1.031688 0.228 11 TAGLN3 0.715 1.030901 0.215 11 ZWINT 0.730 1.019322 0.230 11 KCNC1 0.722 1.018621 0.222 11 CHD3 0.705 1.017770 0.205 11 ATP6V1G2 0.717 1.016268 0.217 11 SNCB 0.769 1.015930 0.269 11 HMGN1 0.215 −1.563282 0.285 11 GNB1 0.242 −1.670947 0.258 11 NEUROD1 0.282 −1.681634 0.218 11 UNC119 0.251 −1.717318 0.249 11 NR2E3 0.268 −1.718845 0.232 11 CNGA1 0.260 −1.814289 0.240 11 ROM1 0.231 −1.833284 0.269 11 SLC24A1 0.277 −1.869445 0.223 11 RPGRIP1 0.248 −1.918559 0.252 11 TULP1 0.204 −1.929248 0.296 11 RS1 0.257 −1.929959 0.243 11 RP1 0.229 −1.939595 0.271 11 NRL 0.255 −1.968506 0.245 11 PRPH2 0.194 −1.989465 0.306 11 PDE6B 0.225 −2.074086 0.275 11 RCVRN 0.214 −2.090257 0.286 11 GNAT1 0.200 −2.097595 0.300 11 RHO 0.164 −2.136073 0.336 11 PDE6G 0.195 −2.169314 0.305 11 PDC 0.164 −2.204122 0.336 11 SAG 0.152 −2.236181 0.348 11 GNGT1 0.154 −2.283434 0.346 11 cluster no. 12 DE = 68 SLC6A1 0.874 2.180099 0.374 12 CBLN2 0.754 1.928113 0.254 12 PAX6 0.828 1.886874 0.328 12 TKT 0.826 1.848995 0.326 12 SNHG11 0.868 1.828275 0.368 12 TFAP2B 0.804 1.768165 0.304 12 NAP1L5 0.824 1.752147 0.324 12 GAD1 0.768 1.707274 0.268 12 PCDH10 0.714 1.651388 0.214 12 SIX3 0.714 1.622442 0.214 12 MEG3 0.863 1.616915 0.363 12 CELF4 0.845 1.583306 0.345 12 ATP1B1 0.822 1.555753 0.322 12 SYNPR 0.745 1.536495 0.245 12 2900011O08RIK 0.770 1.510272 0.270 12 CACNG4 0.753 1.474837 0.253 12 FRMD5 0.749 1.458548 0.249 12 MEIS2 0.722 1.457447 0.222 12 ZFHX3 0.712 1.448061 0.212 12 BASP1 0.781 1.447063 0.281 12 RPH3A 0.721 1.422091 0.221 12 GRIA2 0.795 1.402898 0.295 12 GUCY1A3 0.713 1.393783 0.213 12 DPYSL4 0.718 1.360517 0.218 12 PNMAL2 0.744 1.343839 0.244 12 RUNX1T1 0.713 1.335288 0.213 12 ELAVL3 0.748 1.329163 0.248 12 RAB3C 0.710 1.324800 0.210 12 NRSN1 0.721 1.306849 0.221 12 UCHL1 0.736 1.300785 0.236 12 TTC3 0.832 1.295748 0.332 12 ADARB1 0.723 1.277937 0.223 12 GNG3 0.765 1.263270 0.265 12 NDRG4 0.744 1.253376 0.244 12 A030009H04RIK 0.706 1.252601 0.206 12 SV2A 0.785 1.240701 0.285 12 DUSP26 0.715 1.211692 0.215 12 APC 0.753 1.150275 0.253 12 GPRASP1 0.737 1.147836 0.237 12 GPM6A 0.752 1.141256 0.252 12 TMX4 0.707 1.122604 0.207 12 RTN1 0.749 1.119089 0.249 12 NRXN2 0.709 1.113600 0.209 12 LDHB 0.705 1.097431 0.205 12 NGFRAP1 0.709 1.075985 0.209 12 NDN 0.708 1.061856 0.208 12 BEX2 0.754 1.041420 0.254 12 MARCKS 0.731 1.019699 0.231 12 HMGN1 0.250 −1.195481 0.250 12 GNB1 0.268 −1.266009 0.232 12 RP1 0.280 −1.277829 0.220 12 NR2E3 0.296 −1.336009 0.204 12 RPGRIP1 0.290 −1.341698 0.210 12 RCVRN 0.261 −1.376084 0.239 12 NRL 0.292 −1.393005 0.208 12 UNC119 0.266 −1.397189 0.234 12 PRPH2 0.232 −1.433849 0.268 12 TULP1 0.238 −1.438510 0.262 12 ROM1 0.258 −1.441911 0.242 12 RS1 0.292 −1.451877 0.208 12 PDE6B 0.265 −1.484310 0.235 12 GNAT1 0.238 −1.516590 0.262 12 CNGA1 0.285 −1.525788 0.215 12 RHO 0.205 −1.537955 0.295 12 SAG 0.196 −1.550193 0.304 12 PDC 0.212 −1.561538 0.288 12 GNGT1 0.204 −1.581874 0.296 12 PDE6G 0.228 −1.637557 0.272 12 cluster no. 13 DE = 163 SCG2 0.963 2.746757 0.463 13 LAMP5 0.949 2.686845 0.449 13 TFAP2B 0.960 2.600604 0.460 13 SLC6A1 0.939 2.455520 0.439 13 GAD1 0.910 2.214303 0.410 13 RASGRP1 0.917 2.098422 0.417 13 CBLN2 0.897 2.019754 0.397 13 GAP43 0.868 2.007008 0.368 13 GRIA3 0.912 1.939880 0.412 13 SNHG11 0.940 1.931816 0.440 13 PCDH17 0.888 1.870311 0.388 13 CBLN1 0.848 1.804900 0.348 13 TAGLN3 0.895 1.804474 0.395 13 GM2694 0.836 1.763564 0.336 13 TFAP2A 0.867 1.742085 0.367 13 SPARCL1 0.896 1.727535 0.396 13 PDGFRA 0.838 1.722897 0.338 13 RAB3C 0.902 1.716234 0.402 13 NAP1L5 0.901 1.703198 0.401 13 GUCY1A3 0.893 1.681253 0.393 13 CELF4 0.919 1.676011 0.419 13 SPOCK3 0.903 1.642174 0.403 13 LNX1 0.863 1.623297 0.363 13 SEMA3A 0.816 1.615345 0.316 13 LRRTM1 0.867 1.602351 0.367 13 NSG1 0.838 1.594951 0.338 13 TMEM179 0.841 1.593475 0.341 13 FRMD5 0.886 1.585843 0.386 13 ATP1B1 0.912 1.585191 0.412 13 AI593442 0.845 1.575313 0.345 13 GJC1 0.793 1.560209 0.293 13 CYGB 0.879 1.519598 0.379 13 PHLDA1 0.818 1.515132 0.318 13 MEG3 0.909 1.503058 0.409 13 DPP6 0.886 1.502219 0.386 13 DKK3 0.892 1.481844 0.392 13 KCNIP1 0.855 1.481648 0.355 13 NDRG4 0.878 1.480199 0.378 13 SYN2 0.844 1.477726 0.344 13 SLC32A1 0.856 1.462162 0.356 13 ELAVL4 0.818 1.457094 0.318 13 ISOC1 0.759 1.449689 0.259 13 ALDOC 0.874 1.444666 0.374 13 FNBP1L 0.829 1.440875 0.329 13 ELAVL3 0.862 1.418171 0.362 13 SV2A 0.891 1.416160 0.391 13 GRIA4 0.865 1.408494 0.365 13 RGS17 0.785 1.404754 0.285 13 UCHL1 0.837 1.390501 0.337 13 NRSN1 0.875 1.376384 0.375 13 PTPRM 0.803 1.366832 0.303 13 NSG2 0.858 1.361192 0.358 13 DNM3 0.890 1.359611 0.390 13 CLMP 0.784 1.357481 0.284 13 GNG3 0.838 1.348245 0.338 13 2900011O08RIK 0.845 1.338735 0.345 13 LHX9 0.815 1.337030 0.315 13 VAMP4 0.854 1.335530 0.354 13 CAMKV 0.815 1.331781 0.315 13 DTNBP1 0.846 1.329320 0.346 13 GAD2 0.805 1.326719 0.305 13 ANK3 0.838 1.323306 0.338 13 BASP1 0.865 1.316675 0.365 13 FGF10 0.748 1.308488 0.248 13 STMN3 0.861 1.296175 0.361 13 FUT9 0.783 1.296115 0.283 13 IMPACT 0.842 1.295463 0.342 13 SYT4 0.862 1.289100 0.362 13 PAX6 0.864 1.287430 0.364 13 TENM1 0.790 1.285335 0.290 13 MAPT 0.830 1.283527 0.330 13 RGS8 0.823 1.279287 0.323 13 NECAB1 0.789 1.268538 0.289 13 GRM1 0.751 1.253073 0.251 13 CALN1 0.773 1.247262 0.273 13 CACNA2D2 0.838 1.237957 0.338 13 ZWINT 0.860 1.220447 0.360 13 RBFOX2 0.793 1.217025 0.293 13 OPCML 0.772 1.212407 0.272 13 E130218I03RIK 0.871 1.204019 0.371 13 LMO4 0.803 1.203676 0.303 13 ATP6V1G2 0.820 1.202503 0.320 13 GABRA2 0.761 1.202476 0.261 13 MARCKS 0.867 1.199734 0.367 13 TCEAL5 0.795 1.195481 0.295 13 SYNPR 0.800 1.181298 0.300 13 GABRA3 0.767 1.176700 0.267 13 MLLT11 0.810 1.174360 0.310 13 VSTM2L 0.775 1.171942 0.275 13 A030009H04RIK 0.799 1.167220 0.299 13 ASPH 0.848 1.166139 0.348 13 SNRPN 0.819 1.165623 0.319 13 DNER 0.814 1.158918 0.314 13 TMEM191C 0.811 1.156170 0.311 13 PRKAR1A 0.858 1.150894 0.358 13 TTC3 0.867 1.150786 0.367 13 HPGD 0.742 1.145794 0.242 13 SH3BGRL 0.818 1.143089 0.318 13 TUBB2A 0.858 1.142518 0.358 13 ITM2C 0.855 1.132688 0.355 13 DLG2 0.755 1.127546 0.255 13 EPB4.1L4A 0.758 1.123112 0.258 13 SLC6A5 0.757 1.122854 0.257 13 LSAMP 0.790 1.119316 0.290 13 SLC24A2 0.751 1.117128 0.251 13 RUNX1T1 0.796 1.116379 0.296 13 SNCB 0.829 1.114629 0.329 13 CRABP1 0.723 1.112187 0.223 13 MARCKSL1 0.786 1.109417 0.286 13 NGFRAP1 0.841 1.105288 0.341 13 GRIA2 0.843 1.099977 0.343 13 LDHB 0.836 1.091893 0.336 13 6330403K07RIK 0.716 1.089339 0.216 13 RTN1 0.835 1.088449 0.335 13 CPLX3 0.837 1.084019 0.337 13 PAK3 0.780 1.083627 0.280 13 GNAS 0.836 1.081193 0.336 13 NRXN2 0.809 1.081039 0.309 13 PJA2 0.823 1.077566 0.323 13 VSNL1 0.759 1.077335 0.259 13 PRKCE 0.808 1.072516 0.308 13 TMX4 0.787 1.065684 0.287 13 SYT11 0.821 1.064647 0.321 13 CFL1 0.829 1.063733 0.329 13 STEAP2 0.779 1.060304 0.279 13 ABAT 0.753 1.048614 0.253 13 GM1673 0.772 1.046935 0.272 13 6430548M08RIK 0.807 1.045290 0.307 13 CALM1 0.890 1.044110 0.390 13 VSTM2A 0.755 1.039415 0.255 13 SERP2 0.757 1.039018 0.257 13 DLGAP1 0.758 1.032184 0.258 13 WDR1 0.775 1.031819 0.275 13 BEX2 0.825 1.030974 0.325 13 GRIK2 0.727 1.028371 0.227 13 LINGO1 0.726 1.021154 0.226 13 HSP90AB1 0.835 1.017052 0.335 13 NCALD 0.744 1.014432 0.244 13 NDN 0.803 1.013667 0.303 13 YWHAH 0.767 1.006233 0.267 13 PIP4K2A 0.728 1.006224 0.228 13 GNB1 0.250 −1.654835 0.250 13 HMGN1 0.219 −1.661459 0.281 13 UNC119 0.271 −1.736377 0.229 13 NR2E3 0.259 −1.757150 0.241 13 ROM1 0.226 −2.024256 0.274 13 RS1 0.250 −2.076870 0.250 13 RP1 0.229 −2.081436 0.271 13 NRL 0.264 −2.089632 0.236 13 NEUROD1 0.268 −2.094220 0.232 13 PDC 0.174 −2.144862 0.326 13 PDE6B 0.225 −2.217360 0.275 13 SLC24A1 0.253 −2.275124 0.247 13 CNGA1 0.251 −2.284090 0.249 13 GNAT1 0.190 −2.284263 0.310 13 PRPH2 0.185 −2.326461 0.315 13 RCVRN 0.207 −2.339004 0.293 13 PDE6G 0.200 −2.346579 0.300 13 TULP1 0.192 −2.351777 0.308 13 GNGT1 0.156 −2.405413 0.344 13 SAG 0.153 −2.429798 0.347 13 RHO 0.157 −2.459338 0.343 13 RPGRIP1 0.224 −2.497198 0.276 13 cluster no. 14 DE = 127 CARTPT 0.995 5.703726 0.495 14 TFAP2B 0.971 3.040128 0.471 14 GNG2 0.921 2.521110 0.421 14 GAD1 0.935 2.313316 0.435 14 RAB3C 0.906 2.257741 0.406 14 6430548M08RIK 0.917 2.251898 0.417 14 MARCKS 0.949 2.228788 0.449 14 C1QL1 0.891 2.174893 0.391 14 GPR22 0.860 2.130602 0.360 14 PCP4 0.929 2.085684 0.429 14 2610017I09RIK 0.880 2.047078 0.380 14 4833424O15RIK 0.884 2.046187 0.384 14 ATP1B1 0.930 2.002380 0.430 14 C1QL2 0.851 1.948192 0.351 14 RPH3A 0.886 1.922752 0.386 14 SYT10 0.826 1.921924 0.326 14 CAMK4 0.844 1.906300 0.344 14 ISOC1 0.833 1.836812 0.333 14 SLC35D3 0.829 1.831320 0.329 14 NR4A2 0.816 1.806155 0.316 14 GRIA3 0.827 1.723420 0.327 14 NRXN2 0.841 1.694523 0.341 14 KIT 0.791 1.692597 0.291 14 RPRM 0.787 1.685930 0.287 14 CELF4 0.901 1.684178 0.401 14 PBX1 0.896 1.668218 0.396 14 SYT7 0.822 1.654737 0.322 14 SYT4 0.833 1.635617 0.333 14 KCNIP1 0.830 1.617504 0.330 14 FBXW7 0.841 1.574306 0.341 14 ITM2C 0.876 1.542051 0.376 14 TENM1 0.766 1.538949 0.266 14 NAP1L5 0.860 1.532501 0.360 14 CACNA2D2 0.808 1.530876 0.308 14 GNG3 0.851 1.511727 0.351 14 ELAVL4 0.791 1.506871 0.291 14 POU3F3 0.772 1.496067 0.272 14 TFAP2A 0.793 1.479966 0.293 14 HOMER2 0.725 1.453440 0.225 14 TBX3 0.763 1.424956 0.263 14 CAR8 0.751 1.411188 0.251 14 TSHZ1 0.787 1.379317 0.287 14 BC048943 0.816 1.375829 0.316 14 SLC32A1 0.799 1.373823 0.299 14 CAMKV 0.782 1.366152 0.282 14 PDE3A 0.744 1.357171 0.244 14 CNKSR2 0.725 1.353715 0.225 14 SNHG11 0.855 1.350103 0.355 14 GABRA2 0.753 1.348395 0.253 14 UCHL1 0.839 1.339151 0.339 14 STMN2 0.816 1.321500 0.316 14 AMIGO2 0.761 1.315679 0.261 14 YWHAH 0.814 1.293229 0.314 14 MARCKSL1 0.784 1.286051 0.284 14 ANKS1B 0.759 1.281614 0.259 14 NDRG4 0.813 1.274413 0.313 14 GAP43 0.749 1.266684 0.249 14 AUTS2 0.783 1.256839 0.283 14 SYNPR 0.820 1.249817 0.320 14 ATP2B1 0.869 1.238571 0.369 14 GRM1 0.726 1.231165 0.226 14 CPLX3 0.835 1.226050 0.335 14 EPB4.1L4A 0.746 1.225236 0.246 14 SOBP 0.717 1.225089 0.217 14 LRRN3 0.732 1.221377 0.232 14 CYGB 0.762 1.207406 0.262 14 E530001K10RIK 0.717 1.204510 0.217 14 COL23A1 0.771 1.203158 0.271 14 VSNL1 0.742 1.194754 0.242 14 GM27031 0.701 1.194087 0.201 14 YWHAG 0.788 1.175123 0.288 14 A030009H04RIK 0.773 1.169740 0.273 14 PHACTR3 0.756 1.169124 0.256 14 RYR2 0.743 1.167697 0.243 14 ZCCHC18 0.790 1.167592 0.290 14 NFIA 0.720 1.165989 0.220 14 EFR3A 0.790 1.165206 0.290 14 ELAVL3 0.757 1.164312 0.257 14 SH3BGRL 0.726 1.143244 0.226 14 PAX6 0.795 1.121544 0.295 14 CTNNA2 0.743 1.115757 0.243 14 VAMP4 0.726 1.103188 0.226 14 SCG2 0.843 1.100054 0.343 14 LIN7A 0.801 1.099208 0.301 14 IMPACT 0.745 1.093091 0.245 14 NGFRAP1 0.787 1.076352 0.287 14 ARHGAP20 0.706 1.071535 0.206 14 PODXL2 0.773 1.071004 0.273 14 ID4 0.704 1.060790 0.204 14 SPOCK3 0.761 1.053007 0.261 14 BASP1 0.790 1.045714 0.290 14 GRM5 0.713 1.040980 0.213 14 DPP6 0.740 1.039731 0.240 14 FAM49A 0.706 1.037227 0.206 14 MLLT11 0.749 1.033558 0.249 14 ACOT7 0.736 1.033349 0.236 14 RIT2 0.729 1.029466 0.229 14 6330403K07RIK 0.710 1.028243 0.210 14 SERPINE2 0.706 1.024427 0.206 14 TMSB10 0.795 1.017801 0.295 14 WDR1 0.746 1.015410 0.246 14 SNCB 0.802 1.013614 0.302 14 STMN3 0.751 1.009106 0.251 14 ZEB2 0.734 1.007237 0.234 14 TTC3 0.821 1.005346 0.321 14 TRANK1 0.710 1.001051 0.210 14 HMGN1 0.252 −1.261384 0.248 14 GNB1 0.278 −1.308646 0.222 14 RP1 0.272 −1.542408 0.228 14 UNC119 0.251 −1.556548 0.249 14 RCVRN 0.262 −1.576865 0.238 14 PDE6G 0.243 −1.595753 0.257 14 ROM1 0.252 −1.638720 0.248 14 TULP1 0.238 −1.644062 0.262 14 CNGA1 0.290 −1.645086 0.210 14 PDC 0.207 −1.653719 0.293 14 GNAT1 0.231 −1.672780 0.269 14 PDE6B 0.255 −1.698852 0.245 14 PRPH2 0.219 −1.700665 0.281 14 NRL 0.285 −1.702971 0.215 14 GNGT1 0.191 −1.727437 0.309 14 NR2E3 0.274 −1.735667 0.226 14 SLC24A1 0.284 −1.736471 0.216 14 SAG 0.186 −1.773021 0.314 14 RPGRIP1 0.260 −1.792548 0.240 14 RHO 0.186 −1.793033 0.314 14 RS1 0.268 −1.840885 0.232 14 cluster no. 15 DE = 69 SLC17A8 1.000 3.971625 0.500 15 LAMP5 0.940 2.673730 0.440 15 A930001A20R1K 0.889 2.597410 0.389 15 CAR3 0.835 2.514193 0.335 15 TFAP2B 0.905 2.503643 0.405 15 GRIA3 0.826 2.061066 0.326 15 GABRA2 0.842 2.031614 0.342 15 PCP4 0.909 1.973695 0.409 15 CDC7 0.832 1.955872 0.332 15 SNHG11 0.887 1.937983 0.387 15 VSTM2L 0.832 1.918272 0.332 15 STMN2 0.849 1.904450 0.349 15 CAMK2N1 0.861 1.838364 0.361 15 THSD7A 0.787 1.831897 0.287 15 ITM2B 0.910 1.821993 0.410 15 SPHKAP 0.831 1.715234 0.331 15 RBFOX1 0.735 1.705497 0.235 15 OLFM1 0.839 1.659507 0.339 15 CACNG4 0.815 1.640851 0.315 15 PDE1C 0.762 1.600665 0.262 15 NXPH1 0.756 1.565593 0.256 15 TFAP2A 0.747 1.543928 0.247 15 CELF4 0.841 1.542615 0.341 15 CADM3 0.799 1.512073 0.299 15 SLC24A3 0.743 1.506017 0.243 15 HPGD 0.706 1.448453 0.206 15 GPHN 0.809 1.446959 0.309 15 GNG3 0.818 1.418323 0.318 15 NEUROD2 0.737 1.357057 0.237 15 2900011O08R1K 0.772 1.324460 0.272 15 NXPH3 0.734 1.317785 0.234 15 MARCKS 0.815 1.293783 0.315 15 RAB3C 0.739 1.288257 0.239 15 CDK14 0.739 1.286933 0.239 15 SORCS1 0.717 1.234444 0.217 15 CALM1 0.893 1.233054 0.393 15 A830010M20RIK 0.747 1.230927 0.247 15 SIX6 0.750 1.213235 0.250 15 NSG2 0.731 1.208784 0.231 15 SNCB 0.795 1.174871 0.295 15 NREP 0.813 1.167885 0.313 15 TAGLN3 0.765 1.156591 0.265 15 NSG1 0.707 1.149341 0.207 15 CHGA 0.768 1.148225 0.268 15 MEG3 0.820 1.127476 0.320 15 GRIA2 0.786 1.124935 0.286 15 ELAVL3 0.721 1.121228 0.221 15 NNAT 0.713 1.097324 0.213 15 CALM2 0.818 1.097061 0.318 15 NRXN2 0.746 1.092058 0.246 15 TCEAL5 0.701 1.076692 0.201 15 PGM2L1 0.749 1.071344 0.249 15 RUNX1T1 0.726 1.057725 0.226 15 RTN1 0.787 1.038594 0.287 15 NRXN3 0.757 1.036936 0.257 15 HLF 0.729 1.032907 0.229 15 TTC3 0.783 1.000773 0.283 15 A030009H04RIK 0.710 1.000074 0.210 15 GNAT1 0.299 −1.040079 0.201 15 GNB1 0.299 −1.128978 0.201 15 PDE6G 0.299 −1.189289 0.201 15 PRPH2 0.281 −1.204277 0.219 15 TULP1 0.275 −1.241916 0.225 15 SAG 0.232 −1.259884 0.268 15 ROM1 0.278 −1.321566 0.222 15 RCVRN 0.291 −1.325043 0.209 15 PDC 0.246 −1.329612 0.254 15 GNGT1 0.240 −1.347078 0.260 15 RHO 0.227 −1.442002 0.273 15 cluster no. 16 DE = 97 LAMP5 0.946 2.657760 0.446 16 GJD2 0.928 2.371019 0.428 16 DNER 0.912 2.349418 0.412 16 TFAP2B 0.937 2.307419 0.437 16 SLC6A9 0.877 2.261401 0.377 16 DYNC1I1 0.871 2.238846 0.371 16 CAR2 0.951 2.212296 0.451 16 TMEM132A 0.830 2.024931 0.330 16 HSPA12A 0.882 2.015750 0.382 16 EIF1B 0.886 2.008369 0.386 16 NCALD 0.858 1.949536 0.358 16 RNF152 0.834 1.859704 0.334 16 CALM1 0.949 1.848818 0.449 16 CPLX3 0.910 1.811801 0.410 16 GRIA3 0.813 1.803783 0.313 16 CALB1 0.817 1.799962 0.317 16 ATP1B1 0.887 1.769979 0.387 16 NDRG4 0.841 1.747721 0.341 16 CAMKV 0.808 1.717383 0.308 16 CCSAP 0.761 1.662431 0.261 16 PTPRF 0.775 1.659107 0.275 16 RCAN2 0.772 1.642853 0.272 16 STAC2 0.756 1.591278 0.256 16 DLGAP1 0.780 1.588752 0.280 16 DAB1 0.780 1.587313 0.280 16 SCN1A 0.754 1.580647 0.254 16 SLC24A2 0.728 1.578024 0.228 16 ZYX 0.743 1.492083 0.243 16 NFIA 0.764 1.487009 0.264 16 PROX1 0.827 1.483800 0.327 16 PLCH1 0.751 1.482505 0.251 16 FGF1 0.739 1.462304 0.239 16 ELAVL3 0.781 1.425611 0.281 16 ZFP804A 0.727 1.413891 0.227 16 FSTL5 0.765 1.396307 0.265 16 PHLDA1 0.707 1.389106 0.207 16 PPP1R1A 0.773 1.373425 0.273 16 6430548M08RIK 0.808 1.371200 0.308 16 LSAMP 0.751 1.359807 0.251 16 SPOCK3 0.746 1.352438 0.246 16 KCNMA1 0.784 1.346329 0.284 16 PAK7 0.751 1.343190 0.251 16 ATP6V1G2 0.757 1.336798 0.257 16 KIF5C 0.756 1.296446 0.256 16 TSPAN7 0.854 1.277924 0.354 16 FBXW7 0.753 1.273232 0.253 16 SYNPR 0.759 1.263414 0.259 16 CACNG3 0.704 1.254853 0.204 16 DARC 0.722 1.251447 0.222 16 OSBPL1A 0.724 1.244547 0.224 16 MEG3 0.832 1.232034 0.332 16 SV2A 0.810 1.225364 0.310 16 A030009H04RIK 0.718 1.223471 0.218 16 TAGLN3 0.764 1.222365 0.264 16 ANKS1B 0.716 1.213048 0.216 16 GRIA4 0.728 1.165564 0.228 16 SLC32A1 0.719 1.163084 0.219 16 QDPR 0.713 1.151071 0.213 16 TCEAL5 0.724 1.146794 0.224 16 RIT2 0.742 1.138978 0.242 16 TPI1 0.790 1.128863 0.290 16 DPP6 0.712 1.125116 0.212 16 BNIP3 0.705 1.116162 0.205 16 PODXL2 0.750 1.108498 0.250 16 ZEB2 0.706 1.105841 0.206 16 RAB3C 0.706 1.104219 0.206 16 TUBB2A 0.758 1.097099 0.258 16 PHYHIPL 0.721 1.053411 0.221 16 NSG2 0.707 1.039994 0.207 16 CADM3 0.724 1.033579 0.224 16 PNMAL2 0.726 1.032390 0.226 16 ITM2C 0.757 1.031738 0.257 16 GRIA2 0.742 1.020469 0.242 16 NRXN3 0.740 1.019019 0.240 16 SPHKAP 0.713 1.014996 0.213 16 ANK3 0.715 1.004342 0.215 16 HMGN1 0.215 −1.521153 0.285 16 GNB1 0.229 −1.683458 0.271 16 CNGA1 0.271 −1.708074 0.229 16 UNC119 0.244 −1.715525 0.256 16 RPGRIP1 0.264 −1.737171 0.236 16 ROM1 0.234 −1.740653 0.266 16 NRL 0.265 −1.762281 0.235 16 RS1 0.265 −1.774883 0.235 16 PDE6B 0.237 −1.791541 0.263 16 RP1 0.240 −1.818556 0.260 16 PRPH2 0.201 −1.822970 0.299 16 RCVRN 0.225 −1.829448 0.275 16 PDE6G 0.212 −1.836061 0.288 16 GNGT1 0.177 −1.911169 0.323 16 NR2E3 0.252 −1.935120 0.248 16 SLC24A1 0.268 −1.939047 0.232 16 TULP1 0.202 −1.958300 0.298 16 PDC 0.178 −1.988325 0.322 16 GNAT1 0.203 −2.005405 0.297 16 RHO 0.170 −2.014559 0.330 16 SAG 0.157 −2.131605 0.343 16 cluster no. 17 DE = 99 NHLH2 0.955 2.801308 0.455 17 PTPRF 0.938 2.711222 0.438 17 IGF1 0.893 2.396873 0.393 17 SLC6A9 0.922 2.391729 0.422 17 LAMP5 0.894 2.317776 0.394 17 NECAB1 0.845 2.034798 0.345 17 NFIX 0.842 2.031417 0.342 17 QDPR 0.864 2.017375 0.364 17 RPH3A 0.861 1.948967 0.361 17 TFAP2C 0.804 1.906681 0.304 17 EBF3 0.816 1.897681 0.316 17 ZFP804A 0.806 1.817066 0.306 17 CPLX3 0.918 1.803041 0.418 17 CRABP1 0.796 1.772659 0.296 17 NR2F2 0.779 1.746596 0.279 17 HPCA 0.804 1.734854 0.304 17 ELAVL3 0.846 1.731296 0.346 17 NRSN1 0.810 1.674821 0.310 17 IER5 0.778 1.651591 0.278 17 PTPRT 0.756 1.624019 0.256 17 DAB1 0.802 1.623759 0.302 17 TUBB2A 0.848 1.623271 0.348 17 LGR5 0.757 1.617618 0.257 17 NCALD 0.795 1.603750 0.295 17 VSTM2A 0.740 1.554722 0.240 17 CELF4 0.872 1.530692 0.372 17 SULF2 0.760 1.520666 0.260 17 MGLL 0.754 1.520539 0.254 17 PAX6 0.816 1.498468 0.316 17 SLC24A3 0.781 1.478973 0.281 17 PAM 0.742 1.475693 0.242 17 CABP1 0.775 1.471362 0.275 17 CACNG3 0.735 1.458559 0.235 17 SLC32A1 0.764 1.449804 0.264 17 HS6ST2 0.707 1.397958 0.207 17 THRA 0.819 1.389414 0.319 17 NAV1 0.774 1.379521 0.274 17 SPARCL1 0.752 1.366591 0.252 17 DPP6 0.726 1.359750 0.226 17 TCF4 0.820 1.358693 0.320 17 NECAB2 0.717 1.353128 0.217 17 APP 0.828 1.351232 0.328 17 LY6H 0.730 1.336108 0.230 17 TTC3 0.870 1.328177 0.370 17 SYT4 0.748 1.315258 0.248 17 EBF1 0.701 1.310439 0.201 17 CALB2 0.802 1.299700 0.302 17 TKT 0.761 1.294344 0.261 17 CAMKV 0.709 1.291859 0.209 17 SPHKAP 0.771 1.288256 0.271 17 FSTL5 0.725 1.283969 0.225 17 THY1 0.723 1.277498 0.223 17 SUSD4 0.709 1.255558 0.209 17 GRIA4 0.735 1.236041 0.235 17 4930447C04RIK 0.756 1.222306 0.256 17 SEZ6 0.713 1.213564 0.213 17 FILIP1L 0.701 1.211433 0.201 17 MARCKSL1 0.744 1.207456 0.244 17 ANK3 0.761 1.200975 0.261 17 NRXN3 0.807 1.168788 0.307 17 NDUFC2 0.787 1.159975 0.287 17 GPM6A 0.780 1.143074 0.280 17 ITM2C 0.768 1.128670 0.268 17 SV2A 0.779 1.093330 0.279 17 SNHG11 0.825 1.085316 0.325 17 LSAMP 0.709 1.058888 0.209 17 GAS6 0.767 1.058520 0.267 17 CAMK2N1 0.762 1.055403 0.262 17 SCG3 0.751 1.049366 0.251 17 NSG2 0.706 1.049170 0.206 17 CRMP1 0.709 1.036034 0.209 17 MEG3 0.839 1.025407 0.339 17 NREP 0.775 1.017209 0.275 17 PGRMC1 0.723 1.013992 0.223 17 PPP1R1A 0.707 1.008115 0.207 17 INA 0.720 1.004427 0.220 17 HMGN1 0.257 −1.196552 0.243 17 CST3 0.297 −1.331354 0.203 17 GNB1 0.257 −1.407515 0.243 17 CNGA1 0.284 −1.538745 0.216 17 UNC119 0.258 −1.566938 0.242 17 NEUROD1 0.288 −1.572623 0.212 17 ROM1 0.243 −1.577979 0.257 17 NRL 0.281 −1.587397 0.219 17 PRPH2 0.211 −1.721597 0.289 17 SLC24A1 0.279 −1.725891 0.221 17 TULP1 0.219 −1.731480 0.281 17 RP1 0.244 −1.732042 0.256 17 NR2E3 0.264 −1.737427 0.236 17 RS1 0.266 −1.747543 0.234 17 RCVRN 0.233 −1.757940 0.267 17 RPGRIP1 0.258 −1.766938 0.242 17 PDE6G 0.220 −1.771881 0.280 17 PDE6B 0.236 −1.776243 0.264 17 GNAT1 0.216 −1.796601 0.284 17 PDC 0.191 −1.801008 0.309 17 RHO 0.181 −1.842355 0.319 17 SAG 0.174 −1.848204 0.326 17 GNGT1 0.181 −1.878152 0.319 17 cluster no. 18 DE = 76 myA UC myDiff po wer cl ust NHLH2 0.9 40 2 .577919 440 18 0. PCDH17 0.9 26 2 .518747 426 18 0. NFIX 0.8 96 2 .289617 396 18 0. HPCA 0.8 94 2 .165617 394 18 0. NFIB 0.8 50 2 .151836 350 18 0. CHN2 0.8 65 1 .981338 365 18 0. NECAB1 0.8 34 1 .930261 334 18 0. CELF4 0.9 44 1 .891838 444 18 0. COL12A1 0.7 69 1 .884139 269 18 0. PRDM13 0.7 95 1 .854160 295 18 0. D3BWG0562E 30 1 .829089 330 18 D3 0.8 0. TCF4 0.8 92 1 .827289 392 18 0. NRXN1 0.7 67 1 .826387 267 18 0. SOCS2 0.7 95 1 .761385 295 18 0. ANK3 0.8 44 1 .673902 344 18 0. TFAP2C 0.7 59 1 .629828 259 18 0. STMN2 0.7 49 1 .556131 249 18 0. ZFP804A 0.7 19 1 .551147 219 18 0. APP 0.8 75 1 .546885 375 18 0. ELAVL3 0.7 83 1 .506019 283 18 0. ARHGAP20 0.7 19 1 .505211 219 18 0. MEG3 0.8 80 1 .462490 380 18 0. SLC32A1 0.7 65 1 .444429 265 18 0. NAV1 0.7 62 1 .417519 262 18 0. SEMA4G 0.7 29 1 .383182 229 18 0. MARCKSL1 0.7 76 1 .359972 276 18 0. PIK3R3 0.7 45 1 .354144 245 18 0. THRA 0.8 20 1 .353685 320 18 0. NCALD 0.7 36 1 .337872 236 18 0. NSG1 0.7 42 1 .320977 242 18 0. PTPRS 0.7 43 1 .286383 243 18 0. NREP 0.8 40 1 .285992 340 18 0. CABP1 0.7 25 1 .262818 225 18 0. SIX3 0.7 87 1 .251619 287 18 0. SLC6A9 0.7 10 1 .245464 210 18 0. RPH3A 0.7 52 1 .238243 252 18 0. TTC3 0.8 42 1 .233544 342 18 0. GRIA2 0.7 77 1 .228039 277 18 0. CD47 0.7 53 1 .210868 253 18 0. ATP1B1 0.8 01 1 .167177 301 18 0. ZCCHC18 0.7 20 1 .164194 220 18 0. PLEKHA1 0.7 52 1 .163094 252 18 0. GPM6A 0.7 88 1 .153552 288 18 0. PNMAL2 0.7 48 1 .130451 248 18 0. GRIA4 0.7 25 1 .120343 225 18 0. RTN1 0.7 73 1 .099410 273 18 0. TUBB2A 0.7 69 1 .094495 269 18 0. CAMK2N1 0.7 42 1 .088043 242 18 0. CALM2 0.7 98 1 .074762 298 18 0. TAGLN3 0.7 19 1 .054396 219 18 0. NRXN3 0.7 54 1 .040204 254 18 0. PAX6 0.7 34 1 .034451 234 18 0. NGFRAP1 0.7 39 1 .019194 239 18 0. HMGN1 0.2 51 −1 .229929 249 18 0. CST3 0.2 91 −1 .416269 209 18 0. GNB1 0.2 47 −1 .445738 253 18 0. RPGRIP1 0.2 83 −1 .546356 217 18 0. NRL 0.2 89 −1 .564782 211 18 0. NEUROD1 0.2 83 −1 .620185 217 18 0. NR2E3 0.2 75 −1 .669162 225 18 0. PDE6B 0.2 54 −1 .672037 246 18 0. UNC119 0.2 44 −1 .678390 256 18 0. RP1 0.2 48 −1 .709894 252 18 0. SLC24A1 0.2 87 −1 .717416 213 18 0. PDC 0.1 98 −1 .748702 302 18 0. ROM1 0.2 30 −1 .751335 270 18 0. TULP1 0.2 18 −1 .761842 282 18 0. PDE6G 0.2 26 −1 .763229 274 18 0. RCVRN 0.2 31 −1 .769978 269 18 0. SAG 0.1 84 −1 .774554 316 18 0. CNGA1 0.2 65 −1 .786474 235 18 0. GNGT1 0.1 89 −1 .797833 311 18 0. RS1 0.2 68 −1 .853854 232 18 0. GNAT1 0.2 14 −1 .946696 286 18 0. PRPH2 0.2 02 −1 .956290 298 18 0. RHO 0.1 80 −2 .007748 320 18 0. cluster no. 19 DE = 115 myAUC myDiff power cluster # LAMP5 0.966 2.812286 0.466 19 GABRA1 0.897 2.484680 0.397 19 SLC24A3 0.927 2.393144 0.427 19 NHLH2 0.945 2.383320 0.445 19 LY6H 0.876 2.116752 0.376 19 EBF1 0.874 2.024209 0.374 19 SNHG11 0.922 1.988360 0.422 19 NDRG4 0.876 1.960583 0.376 19 CDH22 0.815 1.785911 0.315 19 SPHKAP 0.886 1.743169 0.386 19 PNMAL2 0.867 1.735673 0.367 19 SIX3 0.851 1.695121 0.351 19 PTPRT 0.803 1.687676 0.303 19 PTGDS 0.783 1.682383 0.283 19 SLC6A9 0.811 1.678064 0.311 19 CAMKV 0.818 1.675469 0.318 19 NRXN2 0.848 1.674929 0.348 19 ELAVL3 0.862 1.653420 0.362 19 PTPRD 0.849 1.648261 0.349 19 SYT13 0.813 1.625862 0.313 19 CHN2 0.797 1.618956 0.297 19 AQP6 0.736 1.613186 0.236 19 CABP1 0.840 1.607853 0.340 19 TCF4 0.879 1.577171 0.379 19 LDHB 0.829 1.565948 0.329 19 RAB3C 0.777 1.545867 0.277 19 PRDM13 0.768 1.521082 0.268 19 INA 0.852 1.511391 0.352 19 SIX6 0.783 1.490271 0.283 19 KCTD8 0.766 1.472089 0.266 19 MEG3 0.905 1.468522 0.405 19 PAX6 0.823 1.451718 0.323 19 APP 0.852 1.450537 0.352 19 OGFRL1 0.821 1.437451 0.321 19 ATP1B1 0.855 1.426796 0.355 19 6430548M08RIK 0.803 1.419158 0.303 19 NECAB1 0.749 1.374682 0.249 19 VAT1L 0.743 1.371743 0.243 19 NNAT 0.722 1.357449 0.222 19 NRSN1 0.790 1.356337 0.290 19 DPP6 0.765 1.355499 0.265 19 NSG1 0.771 1.344649 0.271 19 TKT 0.806 1.341063 0.306 19 CDK14 0.761 1.337859 0.261 19 FRRS1L 0.709 1.335420 0.209 19 OSBPL1A 0.752 1.329635 0.252 19 MGLL 0.763 1.294623 0.263 19 GABRG2 0.757 1.291374 0.257 19 GNG3 0.828 1.268832 0.328 19 GRIA2 0.830 1.263501 0.330 19 BASP1 0.810 1.253882 0.310 19 STMN3 0.809 1.238855 0.309 19 GAS7 0.711 1.233308 0.211 19 CELF4 0.831 1.232486 0.331 19 SPOCK3 0.771 1.231314 0.271 19 DLG2 0.718 1.209247 0.218 19 STMN4 0.732 1.207910 0.232 19 ZFP804A 0.711 1.206180 0.211 19 SPARCL1 0.760 1.196819 0.260 19 THRA 0.783 1.194146 0.283 19 MLLT11 0.751 1.190315 0.251 19 GRIA3 0.722 1.173347 0.222 19 TCEAL5 0.740 1.171672 0.240 19 GABRB2 0.707 1.167103 0.207 19 LHFP 0.721 1.165278 0.221 19 HMGCS1 0.731 1.155608 0.231 19 UBASH3B 0.710 1.154651 0.210 19 TMEM215 0.764 1.134491 0.264 19 TAGLN3 0.797 1.134360 0.297 19 HSD17B12 0.778 1.130471 0.278 19 SLC32A1 0.733 1.119009 0.233 19 ABAT 0.708 1.118345 0.208 19 CALM2 0.829 1.105143 0.329 19 ATPIF1 0.795 1.102584 0.295 19 GNAS 0.833 1.076868 0.333 19 SYT4 0.779 1.071587 0.279 19 TTC3 0.832 1.066694 0.332 19 CAMK2N1 0.768 1.054984 0.268 19 TUBB2A 0.780 1.041674 0.280 19 RIT2 0.712 1.039586 0.212 19 PIK3R3 0.717 1.034720 0.217 19 SV2A 0.772 1.033485 0.272 19 CAMK2A 0.701 1.028493 0.201 19 NGFRAP1 0.770 1.026982 0.270 19 A030009H04RIK 0.719 1.026299 0.219 19 GPM6A 0.792 1.021982 0.292 19 NAP1L5 0.759 1.016867 0.259 19 MAPT 0.724 1.007727 0.224 19 NDN 0.717 1.007103 0.217 19 ATP6V1G2 0.711 1.000069 0.211 19 CST3 0.295 −1.464654 0.205 19 GNB1 0.232 −1.731344 0.268 19 HMGN1 0.193 −1.764691 0.307 19 FAM57B 0.287 −1.846219 0.213 19 UNC119 0.235 −1.869744 0.265 19 AIPL1 0.299 −1.912009 0.201 19 NEUROD1 0.267 −1.949197 0.233 19 CNGA1 0.258 −2.018138 0.242 19 ROM1 0.220 −2.018487 0.280 19 RP1 0.224 −2.025188 0.276 19 NR2E3 0.250 −2.051380 0.250 19 PDE6B 0.216 −2.080446 0.284 19 RS1 0.250 −2.098084 0.250 19 PRPH2 0.185 −2.112352 0.315 19 RCVRN 0.209 −2.119590 0.291 19 SLC24A1 0.260 −2.160451 0.240 19 NRL 0.243 −2.171600 0.257 19 PDE6G 0.194 −2.189464 0.306 19 TULP1 0.190 −2.209461 0.310 19 GNAT1 0.190 −2.263062 0.310 19 SAG 0.144 −2.317843 0.356 19 GNGT1 0.153 −2.323731 0.347 19 RHO 0.162 −2.332956 0.338 19 RPGRIP1 0.228 −2.401760 0.272 19 PDC 0.156 −2.470951 0.344 19 Diff myAUC my power cluster # ge cluster no. 20 DE = 43 PPP1R17 0.909 3.02 8071 20 PPP1R 0.409 EBF3 0.791 2.15 8191 20 EB 0.291 LGR5 0.772 2.11 3992 20 LG 0.272 EBF1 0.743 1.97 8420 20 EB 0.243 IGFBP5 0.726 1.93 2417 20 IGFB 0.226 TCF4 0.834 1.74 0057 20 TC 0.334 PNMAL2 0.785 1.72 2746 20 PNMA 0.285 ZFP804A 0.714 1.71 2913 20 ZFP80 0.214 ELAVL3 0.751 1.65 7175 20 ELAV 0.251 SNCA 0.723 1.63 1290 20 SN 0.223 LY6H 0.712 1.60 0690 20 LY 0.212 INA 0.743 1.58 6423 20 I 0.243 CACNG4 0.702 1.42 8349 20 CACN 0.202 MARCKS 0.777 1.38 4398 20 MARC 0.277 GRIA2 0.746 1.29 4026 20 GRI 0.246 SPHKAP 0.722 1.28 5598 20 SPHK 0.222 CALB2 0.719 1.27 4202 20 CAL 0.219 MEG3 0.813 1.26 2649 20 ME 0.313 BASP1 0.724 1.23 1810 20 BAS 0.224 RTN1 0.750 1.22 6570 20 RT 0.250 CELF4 0.769 1.22 0263 20 CEL 0.269 NEUROD4 0.719 1.14 3353 20 NEURO 0.219 GNG3 0.711 1.10 4068 20 GN 0.211 SYT1 0.807 1.04 4134 20 SY 0.307 TTC3 0.768 1.02 9450 20 TT 0.268 HMGN1 0.273 −1.01 4397 20 HMG 0.227 GNB1 0.284 −1.07 5547 20 GN 0.216 ROM1 0.274 −1.14 4841 20 RO 0.226 UNC119 0.276 −1.17 8787 20 UNC1 0.224 GNAT1 0.261 −1.19 3799 20 GNA 0.239 GNGT1 0.225 −1.28 0242 20 GNG 0.275 PDE6G 0.253 −1.28 9229 20 PDE 0.247 PRPH2 0.237 −1.30 6099 20 PRP 0.263 RP1 0.270 −1.31 2369 20 R 0.230 RHO 0.219 −1.31 4846 20 R 0.281 RCVRN 0.254 −1.31 7434 20 RCV 0.246 RS1 0.290 −1.31 7916 20 R 0.210 PDC 0.224 −1.32 7322 20 P 0.276 TULP1 0.243 −1.33 8314 20 TUL 0.257 PDE6B 0.266 −1.34 2076 20 PDE 0.234 CNGA1 0.283 −1.37 9756 20 CNG 0.217 RPGRIP1 0.282 −1.42 3791 20 RPGRI 0.218 SAG 0.196 −1.47 6851 20 S 0.304 cluster no. 21 DE = 45 NHLH2 0.943 3.05 4281 21 NHL 0.443 NFIX 0.847 2.29 9079 21 NF 0.347 CRABP1 0.842 2.27 6418 21 CRAB 0.342 CCK 0.742 2.07 4822 21 C 0.242 GRIK2 0.782 2.07 0961 21 GRI 0.282 HPCA 0.803 2.00 5328 21 HP 0.303 ELAVL3 0.824 1.88 8644 21 ELAV 0.324 PRKCB 0.802 1.86 1453 21 PRK 0.302 CNTN6 0.738 1.83 6086 21 CNT 0.238 NCKAP5 0.741 1.83 1134 21 NCKA 0.241 LGR5 0.714 1.74 8355 21 LG 0.214 EBF1 0.734 1.71 4978 21 EB 0.234 NRXN1 0.724 1.69 3385 21 NRX 0.224 CELF4 0.853 1.68 7320 21 CEL 0.353 TCF4 0.839 1.67 9788 21 TC 0.339 PRDM13 0.709 1.67 9478 21 PRDM 0.209 CHN2 0.721 1.62 1249 21 CH 0.221 GNAL 0.708 1.58 7639 21 GN 0.208 KCND3 0.701 1.57 6876 21 KCN 0.201 ZFP804A 0.710 1.56 4564 21 ZFP80 0.210 SLC24A3 0.751 1.54 4541 21 SLC24 0.251 APC 0.810 1.49 8604 21 A 0.310 ANK3 0.775 1.40 2792 21 AN 0.275 CAMK2N1 0.768 1.37 7643 21 CAMK2 0.268 PNMAL2 0.740 1.36 3728 21 PNMA 0.240 GRIA2 0.765 1.32 0910 21 GRI 0.265 SPHKAP 0.749 1.30 6807 21 SPHK 0.249 CALM2 0.811 1.26 1951 21 CAL 0.311 MEG3 0.844 1.24 7565 21 ME 0.344 APP 0.745 1.13 2361 21 A 0.245 TTC3 0.794 1.11 6633 21 TT 0.294 GPM6A 0.706 1.10 4331 21 GPM 0.206 UNC119 0.296 −1.16 3773 21 UNC1 0.204 RP1 0.295 −1.18 0835 21 R 0.205 ROM1 0.285 −1.18 8372 21 RO 0.215 PDE6G 0.275 −1.19 3017 21 PDE 0.225 PDE6B 0.286 −1.24 4644 21 PDE 0.214 TULP1 0.264 −1.25 7146 21 TUL 0.236 RHO 0.234 −1.26 0099 21 R 0.266 RCVRN 0.272 −1.27 2038 21 RCV 0.228 SAG 0.226 −1.28 7003 21 S 0.274 PRPH2 0.250 −1.29 1410 21 PRP 0.250 PDC 0.235 −1.29 4464 21 P 0.265 GNGT1 0.234 −1.29 5401 21 GNG 0.266 GNAT1 0.262 −1.29 9206 21 GNA 0.238 myAUC myDiff power cluster # cluster no. 22 DE = 51 LAMP5 0.944 2.824713 0.444 22 TFAP2B 0.872 2.223340 0.372 22 CACNG4 0.834 1.969710 0.334 22 ZFP804A 0.751 1.834667 0.251 22 DPP6 0.764 1.729152 0.264 22 GRIA1 0.718 1.703132 0.218 22 NEUROD2 0.712 1.641371 0.212 22 CELF4 0.860 1.622471 0.360 22 PAX6 0.803 1.597197 0.303 22 SLC6A9 0.760 1.571800 0.260 22 MEG3 0.866 1.469502 0.366 22 2900011O08RIK 0.729 1.446080 0.229 22 ELAVL3 0.749 1.390450 0.249 22 RAB3C 0.713 1.382919 0.213 22 NRSN1 0.702 1.336043 0.202 22 PNMAL2 0.747 1.334122 0.247 22 TCF4 0.788 1.329726 0.288 22 GRIA2 0.780 1.313318 0.280 22 MARCKSL1 0.716 1.298641 0.216 22 SLC32A1 0.704 1.296328 0.204 22 SNHG11 0.784 1.279440 0.284 22 MAPT 0.702 1.244966 0.202 22 NRXN2 0.701 1.211145 0.201 22 GNG3 0.760 1.195139 0.260 22 NAP1L5 0.724 1.176074 0.224 22 TTC3 0.805 1.171854 0.305 22 TAGLN3 0.710 1.156623 0.210 22 PTPRD 0.717 1.099502 0.217 22 BASP1 0.727 1.088889 0.227 22 THRA 0.713 1.076652 0.213 22 SV2A 0.747 1.060584 0.247 22 SNCB 0.757 1.048856 0.257 22 PLEKHA1 0.702 1.025231 0.202 22 GPM6A 0.708 1.019030 0.208 22 HMGN1 0.273 −1.040507 0.227 22 GNB1 0.285 −1.170599 0.215 22 PDE6B 0.293 −1.193748 0.207 22 RP1 0.295 −1.213102 0.205 22 UNC119 0.278 −1.263254 0.222 22 PRPH2 0.245 −1.315824 0.255 22 PDE6G 0.264 −1.337712 0.236 22 RPGRIP1 0.288 −1.352712 0.212 22 PDC 0.226 −1.365109 0.274 22 TULP1 0.248 −1.369050 0.252 22 GNAT1 0.254 −1.389822 0.246 22 NR2E3 0.292 −1.390392 0.208 22 ROM1 0.261 −1.428090 0.239 22 GNGT1 0.219 −1.443382 0.281 22 RCVRN 0.259 −1.447967 0.241 22 SAG 0.208 −1.466971 0.292 22 RHO 0.216 −1.494424 0.284 22 cluster no. 23 DE = 67 TFAP2B 0.928 2.494440 0.428 23 GAD1 0.917 2.437951 0.417 23 FBXW7 0.917 2.420581 0.417 23 2610017I09RIK 0.846 2.309127 0.346 23 PCP4 0.938 2.265534 0.438 23 SLC6A1 0.885 2.235858 0.385 23 DKK3 0.939 2.182791 0.439 23 CELF4 0.935 2.157447 0.435 23 GUCY1A3 0.889 2.108061 0.389 23 SIX3 0.889 2.095564 0.389 23 C1QL2 0.822 2.067956 0.322 23 GUCY1B3 0.865 2.029309 0.365 23 CBFA2T3 0.786 2.026242 0.286 23 POU3F3 0.772 1.859852 0.272 23 NAP1L5 0.860 1.807160 0.360 23 TKT 0.836 1.783663 0.336 23 HPGD 0.751 1.778162 0.251 23 SNHG11 0.895 1.776925 0.395 23 ADARB1 0.803 1.745295 0.303 23 GAD2 0.747 1.658875 0.247 23 LRRN3 0.768 1.658143 0.268 23 CACNG4 0.822 1.640376 0.322 23 OLFM1 0.774 1.633329 0.274 23 MEG3 0.894 1.607437 0.394 23 ELAVL4 0.717 1.469508 0.217 23 KCNIP1 0.731 1.459041 0.231 23 KCND3 0.724 1.426750 0.224 23 ELAVL3 0.756 1.383776 0.256 23 SLC32A1 0.738 1.352046 0.238 23 GNG3 0.797 1.337489 0.297 23 NDRG4 0.760 1.318015 0.260 23 HAP1 0.735 1.314020 0.235 23 FRMD5 0.721 1.311942 0.221 23 APC 0.800 1.285337 0.300 23 TMX4 0.759 1.279036 0.259 23 SCG2 0.808 1.243538 0.308 23 GRIA2 0.774 1.215973 0.274 23 LDHB 0.727 1.201661 0.227 23 TTC3 0.838 1.197850 0.338 23 BASP1 0.772 1.194948 0.272 23 MARCKSL1 0.704 1.159591 0.204 23 GPRASP1 0.738 1.153237 0.238 23 PAX6 0.748 1.152232 0.248 23 HSD17B12 0.736 1.142303 0.236 23 SIX3OS1 0.721 1.135949 0.221 23 IMPACT 0.704 1.129338 0.204 23 6430548M08RIK 0.708 1.125889 0.208 23 TRIM9 0.711 1.124665 0.211 23 TAGLN3 0.728 1.095091 0.228 23 SNCB 0.779 1.067869 0.279 23 HMGN1 0.286 −1.009380 0.214 23 GNB1 0.270 −1.275684 0.230 23 UNC119 0.280 −1.310248 0.220 23 ROM1 0.271 −1.317239 0.229 23 RPGRIP1 0.295 −1.333701 0.205 23 TULP1 0.243 −1.374221 0.257 23 PDE6G 0.255 −1.375311 0.245 23 RCVRN 0.266 −1.381017 0.234 23 PRPH2 0.236 −1.387400 0.264 23 PDE6B 0.278 −1.393976 0.222 23 RP1 0.274 −1.402082 0.226 23 RS1 0.293 −1.450358 0.207 23 GNGT1 0.219 −1.451672 0.281 23 RHO 0.212 −1.459768 0.288 23 SAG 0.209 −1.461985 0.291 23 PDC 0.215 −1.492115 0.285 23 GNAT1 0.243 −1.525967 0.257 23 cluster no. 24 DE = 49 yDiff myAUC m powe r clus t RHO 0.945 1.8 57266 5 2 4 0.44 GNAT1 0.889 1.7 80155 9 2 4 G 0.38 SLC24A1 0.802 1.7 43717 2 2 4 SLC 0.30 PDE6B 0.855 1.7 43134 5 2 4 P 0.35 PDC 0.919 1.7 00660 9 2 4 0.41 CNGA1 0.812 1.6 80377 2 2 4 C 0.31 RP1 0.840 1.6 73527 0 2 4 0.34 SAG 0.930 1.6 50156 0 2 4 0.43 NR2E3 0.810 1.6 44369 0 2 4 N 0.31 NRL 0.808 1.6 44321 8 2 4 0.30 GNB1 0.867 1.6 19807 7 2 4 0.36 GNGT1 0.902 1.6 08430 2 2 4 G 0.40 PRPH2 0.880 1.5 97904 0 2 4 P 0.38 PDE6A 0.737 1.5 88021 7 2 4 P 0.23 PDE6G 0.856 1.5 58813 6 2 4 P 0.35 RCVRN 0.842 1.5 36418 2 2 4 R 0.34 RPGRIP1 0.794 1.5 33882 4 2 4 RPG 0.29 RS1 0.790 1.5 19606 0 2 4 0.29 GUCA1B 0.707 1.5 06131 7 2 4 GU 0.20 CNGB1 0.715 1.4 95706 5 2 4 C 0.21 ROM1 0.820 1.4 77666 0 2 4 0.32 RDH12 0.704 1.4 27972 4 2 4 R 0.20 FAM57B 0.731 1.3 66885 1 2 4 FA 0.23 TULP1 0.835 1.3 49889 5 2 4 T 0.33 AIPL1 0.706 1.1 64169 6 2 4 A 0.20 HMGN1 0.797 1.1 36452 7 2 4 H 0.29 UNC119 0.732 1.0 69530 2 2 4 UN 0.23 SERINC1 0.281 −1.0 08401 9 2 4 SER 0.21 BEX2 0.291 −1.0 15902 9 2 4 0.20 ITM2B 0.266 −1.0 43926 4 2 4 I 0.23 YWHAB 0.253 −1.0 51200 7 2 4 Y 0.24 MAP4 0.290 −1.0 88812 0 2 4 0.21 HSP90AB1 0.209 −1.1 88043 1 2 4 HSP9 0.29 GNAS 0.229 −1.2 07829 1 2 4 0.27 TMSB10 0.290 −1.3 40497 0 2 4 TM 0.21 HMGN3 0.283 −1.3 53477 7 2 4 H 0.21 SCG3 0.286 −1.3 66486 4 2 4 0.21 CPLX3 0.261 −1.4 40524 9 2 4 C 0.23 TTC3 0.215 −1.4 59532 5 2 4 0.28 CELF4 0.277 −1.4 77617 3 2 4 C 0.22 ITM2C 0.274 −1.5 36542 6 2 4 I 0.22 GPM6A 0.282 −1.6 04191 8 2 4 G 0.21 PTPRD 0.290 −1.6 22257 0 2 4 P 0.21 APP 0.289 −1.6 28911 1 2 4 0.21 NRXN3 0.262 −1.6 82084 8 2 4 N 0.23 NME1 0.253 −1.6 87771 7 2 4 0.24 GNAO1 0.225 −1.9 02619 5 2 4 G 0.27 CALM1 0.173 −1.9 04185 7 2 4 C 0.32 MEG3 0.178 −2.1 49534 2 2 4 0.32 myAUC myDiff power cluster # cluster no. 25 DE = 14 PDE6H 0.981 3.791576 0.481 25 OPN1SW 0.832 3.587490 0.332 25 GNGT2 0.964 3.261674 0.464 25 OPN1MW 0.891 3.211129 0.391 25 ARR3 0.918 3.071492 0.418 25 GNAT2 0.941 3.020245 0.441 25 PDE6C 0.879 2.613656 0.379 25 KCNE2 0.853 2.337871 0.353 25 GUCA1A 0.881 1.790297 0.381 25 CD59A 0.725 1.742573 0.225 25 CCDC136 0.730 1.673432 0.230 25 GNB3 0.831 1.569696 0.331 25 SCG3 0.756 1.297292 0.256 25 4930447C04RIK 0.703 1.275268 0.203 25 cluster no. 26 DE = 87 PCP2 0.988 3.533209 0.488 26 TRPM1 0.990 3.445746 0.490 26 GNG13 0.968 2.839805 0.468 26 ISL1 0.948 2.719519 0.448 26 CAR8 0.913 2.699407 0.413 26 PRKCA 0.937 2.609664 0.437 26 GPR179 0.900 2.431366 0.400 26 CALM1 0.988 2.421322 0.488 26 QPCT 0.875 2.374165 0.375 26 VSX2 0.895 2.348176 0.395 26 PCP4 0.945 2.313286 0.445 26 GRM6 0.873 2.232811 0.373 26 GNAO1 0.948 2.215946 0.448 26 LRTM1 0.886 2.179512 0.386 26 TRNP1 0.855 2.159361 0.355 26 CACNA2D3 0.803 2.101927 0.303 26 NME1 0.917 2.066828 0.417 26 GM4792 0.870 2.059987 0.370 26 LIN7A 0.875 2.018521 0.375 26 PROX1 0.850 2.002116 0.350 26 ABLIM1 0.874 1.975136 0.374 26 CABP5 0.840 1.934630 0.340 26 VSTM2B 0.782 1.934535 0.282 26 STRIP2 0.761 1.913167 0.261 26 SEBOX 0.763 1.858373 0.263 26 RPA1 0.790 1.856293 0.290 26 CCDC136 0.803 1.850276 0.303 26 CHGB 0.903 1.837030 0.403 26 B3GALT2 0.775 1.744162 0.275 26 MAP4 0.873 1.732032 0.373 26 RNF152 0.743 1.723092 0.243 26 ZBTB20 0.807 1.707863 0.307 26 CNTN4 0.737 1.705791 0.237 26 IFT20 0.804 1.668409 0.304 26 CASP7 0.725 1.663103 0.225 26 TMSB10 0.844 1.659561 0.344 26 ITM2C 0.829 1.654655 0.329 26 NDNF 0.746 1.643132 0.246 26 TGFB2 0.782 1.633774 0.282 26 GNB3 0.844 1.600635 0.344 26 PTPRD 0.810 1.574528 0.310 26 CLTB 0.779 1.568857 0.279 26 PRDM8 0.706 1.551374 0.206 26 CAR10 0.758 1.546273 0.258 26 NEUROD4 0.787 1.443959 0.287 26 KCNMA1 0.746 1.443881 0.246 26 GABRR1 0.702 1.424760 0.202 26 MAP6 0.704 1.389962 0.204 26 CPLX3 0.833 1.368767 0.333 26 CNTNAP2 0.705 1.357327 0.205 26 REV3L 0.745 1.315953 0.245 26 HMGN3 0.760 1.309377 0.260 26 HSPA12A 0.710 1.264275 0.210 26 CAMSAP2 0.701 1.226712 0.201 26 PPP3CA 0.768 1.224280 0.268 26 ANK3 0.715 1.182166 0.215 26 DNAJA1 0.713 1.141870 0.213 26 ZFP365 0.701 1.138197 0.201 26 APLP2 0.840 1.116573 0.340 26 ATP2B1 0.827 1.109752 0.327 26 2010107E04RIK 0.807 1.078386 0.307 26 GLS 0.729 1.030787 0.229 26 MACF1 0.729 1.028031 0.229 26 NRXN3 0.726 1.013354 0.226 26 ROM1 0.247 −1.535062 0.253 26 CST3 0.258 −1.542458 0.242 26 PRPH2 0.199 −1.778911 0.301 26 FAM57B 0.266 −1.812713 0.234 26 AIPL1 0.282 −1.854518 0.218 26 PDE6A 0.288 −1.862827 0.212 26 NRL 0.236 −1.917565 0.264 26 SLC24A1 0.247 −1.968291 0.253 26 CNGA1 0.234 −1.993041 0.266 26 NR2E3 0.232 −2.003551 0.268 26 RS1 0.232 −2.056603 0.268 26 TULP1 0.179 −2.057501 0.321 26 RP1 0.204 −2.067114 0.296 26 GNAT1 0.181 −2.080109 0.319 26 PDE6B 0.199 −2.104653 0.301 26 RPGRIP1 0.217 −2.108733 0.283 26 PDE6G 0.180 −2.114659 0.320 26 GNB1 0.165 −2.145253 0.335 26 RCVRN 0.188 −2.149677 0.312 26 GNGT1 0.146 −2.187446 0.354 26 RHO 0.143 −2.216846 0.357 26 SAG 0.133 −2.285265 0.367 26 PDC 0.141 −2.289428 0.359 26 cluster no. 27 DE = 27 yDiff myAUC m powe r clus t GRIK1 0.916 3.0 10898 6 2 7 G 0.41 GSG1 0.872 2.6 94718 2 2 7 0.37 OTOR 0.811 2.5 74650 1 2 7 0.31 NNAT 0.810 2.5 09846 0 2 7 0.31 FAM19A3 0.808 2.1 90301 8 2 7 FAM 0.30 SLITRK6 0.722 1.9 42192 2 2 7 SLI 0.22 LHX4 0.775 1.9 30099 5 2 7 0.27 PCP4 0.841 1.6 79884 1 2 7 0.34 PHYHIPL 0.742 1.6 59067 2 2 7 PHY 0.24 SPHKAP 0.770 1.6 43517 0 2 7 SP 0.27 CACNA2D1 1.6 00775 7 2 7 CACN 0.707 0.20 CABP5 0.756 1.5 47771 6 2 7 C 0.25 SCGN 0.711 1.5 42676 1 2 7 0.21 BC030499 0.704 1.4 55994 4 2 7 BC03 0.20 LRTM1 0.754 1.4 08952 4 2 7 L 0.25 NME1 0.777 1.3 17789 7 2 7 0.27 CADPS 0.715 1.2 34166 5 2 7 C 0.21 NEUROD4 0.732 1.2 22587 2 2 7 NEU 0.23 VSX2 0.709 1.1 50766 9 2 7 0.20 NRXN3 0.718 1.1 38040 8 2 7 N 0.21 APP 0.733 1.1 24188 3 2 7 0.23 PRPH2 0.281 −1.0 16461 9 2 7 P 0.21 SAG 0.255 −1.0 43850 5 2 7 0.24 GNAT1 0.289 −1.0 51331 1 2 7 G 0.21 RCVRN 0.299 −1.0 59446 1 2 7 R 0.20 PDC 0.261 −1.0 75620 9 2 7 0.23 RHO 0.261 −1.0 94430 9 2 7 0.23 myAUC myDiff power cluster # cluster no. 28 DE = 48 SLIT2 0.911 2.494784 0.411 28 SCGN 0.910 2.432819 0.410 28 CDH8 0.874 2.307964 0.374 28 SCG2 0.930 2.181234 0.430 28 ZFHX4 0.851 2.178856 0.351 28 VSX1 0.779 1.895571 0.279 28 NETO1 0.751 1.891687 0.251 28 GABRA1 0.861 1.787593 0.361 28 PDE1A 0.752 1.652362 0.252 28 NEUROD4 0.854 1.610346 0.354 28 GRIA2 0.837 1.599678 0.337 28 CADPS 0.828 1.587531 0.328 28 CHRNA6 0.747 1.566433 0.247 28 NTNG1 0.770 1.535756 0.270 28 IGF1 0.745 1.475532 0.245 28 TACR3 0.706 1.466025 0.206 28 LRTM1 0.810 1.446170 0.310 28 LHX4 0.769 1.437311 0.269 28 GRIK1 0.740 1.435103 0.240 28 TNNT1 0.717 1.388436 0.217 28 PTPRD 0.808 1.388278 0.308 28 THSD7A 0.765 1.381783 0.265 28 ESAM 0.708 1.372116 0.208 28 A730046J19RIK 0.711 1.372055 0.211 28 NRXN3 0.819 1.340482 0.319 28 SPHKAP 0.761 1.298899 0.261 28 GLRA1 0.711 1.292095 0.211 28 CAR10 0.758 1.238441 0.258 28 BC030499 0.717 1.204192 0.217 28 PGM2L1 0.735 1.189284 0.235 28 TMEM215 0.713 1.158325 0.213 28 PCP4L1 0.717 1.150546 0.217 28 GUCY1B3 0.726 1.146479 0.226 28 CNTN1 0.713 1.136843 0.213 28 FRMD3 0.704 1.067778 0.204 28 SAMSN1 0.719 1.063324 0.219 28 HMGN3 0.745 1.013834 0.245 28 APP 0.761 1.009757 0.261 28 GNB1 0.281 −1.213742 0.219 28 PRPH2 0.262 −1.246574 0.238 28 TULP1 0.270 −1.255972 0.230 28 RCVRN 0.288 −1.299880 0.212 28 GNGT1 0.251 −1.319077 0.249 28 GNAT1 0.263 −1.357660 0.237 28 SAG 0.220 −1.392674 0.280 28 PDE6G 0.262 −1.411349 0.238 28 PDC 0.228 −1.424123 0.272 28 RHO 0.225 −1.457060 0.275 28 cluster no. 29 DE = 39 SLIT2 0.817 2.116591 0.317 29 GABRA1 0.832 2.006228 0.332 29 PCDH17 0.702 1.882124 0.202 29 WLS 0.708 1.845341 0.208 29 PCDH10 0.727 1.819161 0.227 29 ZFHX4 0.726 1.772755 0.226 29 GLRA1 0.744 1.767981 0.244 29 A730046J19RIK 0.706 1.664659 0.206 29 SLC24A3 0.739 1.605346 0.239 29 NRXN3 0.824 1.586941 0.324 29 KCNMA1 0.754 1.572355 0.254 29 FAM19A3 0.708 1.512326 0.208 29 CABP5 0.747 1.504850 0.247 29 TMEM215 0.728 1.483366 0.228 29 PHYHIPL 0.738 1.470131 0.238 29 PTPRD 0.786 1.461384 0.286 29 SPHKAP 0.755 1.453958 0.255 29 CADPS 0.761 1.431341 0.261 29 MEG3 0.863 1.430565 0.363 29 LRTM1 0.754 1.359036 0.254 29 THSD7A 0.703 1.356030 0.203 29 NEUROD4 0.762 1.294731 0.262 29 NME1 0.772 1.192665 0.272 29 VSX2 0.726 1.181568 0.226 29 SCG3 0.707 1.048887 0.207 29 APP 0.718 1.021875 0.218 29 ROM1 0.288 −1.125274 0.212 29 PDE6B 0.285 −1.189444 0.215 29 RP1 0.293 −1.195551 0.207 29 TULP1 0.261 −1.220904 0.239 29 GNB1 0.262 −1.221726 0.238 29 PRPH2 0.250 −1.249883 0.250 29 SAG 0.222 −1.257367 0.278 29 GNAT1 0.257 −1.311936 0.243 29 RCVRN 0.261 −1.362927 0.239 29 PDC 0.222 −1.366220 0.278 29 RHO 0.220 −1.378427 0.280 29 PDE6G 0.248 −1.428805 0.252 29 GNGT1 0.220 −1.431648 0.280 29 Diff myAUC my power cluster # ge cluster no. 30 DE = 60 NFIA 0.850 2.23 6944 30 NF 0.350 NEUROD4 0.909 2.12 5019 30 NEURO 0.409 LHX4 0.870 2.05 9044 30 LH 0.370 EPHA7 0.805 1.93 1362 30 EPH 0.305 CABP5 0.825 1.86 8986 30 CAB 0.325 HLF 0.786 1.81 5451 30 H 0.286 PTPRZ1 0.786 1.75 6697 30 PTPR 0.286 ATP2B1 0.923 1.72 8502 30 ATP2 0.423 TMEM215 0.810 1.69 9286 30 TMEM2 0.310 CDH9 0.714 1.66 4116 30 CD 0.214 LMO4 0.794 1.64 8088 30 LM 0.294 SULF2 0.759 1.64 4281 30 SUL 0.259 GUCY1A3 0.809 1.60 6336 30 GUCY1 0.309 SYT4 0.797 1.59 7987 30 SY 0.297 GM4792 0.762 1.58 3695 30 GM47 0.262 GRM6 0.776 1.56 8053 30 GR 0.276 CAR10 0.794 1.53 4313 30 CAR 0.294 GABRR2 0.714 1.51 5185 30 GABR 0.214 NDNF 0.753 1.50 7846 30 ND 0.253 NRXN3 0.829 1.50 5059 30 NRX 0.329 KCNG4 0.701 1.48 2390 30 KCN 0.201 GNAO1 0.862 1.44 6431 30 GNA 0.362 VIPR2 0.733 1.42 0776 30 VIP 0.233 FRMD3 0.751 1.40 6949 30 FRM 0.251 SAMSN1 0.748 1.40 4241 30 SAMS 0.248 THSD7A 0.753 1.40 1838 30 THSD 0.253 SOX4 0.722 1.35 3433 30 SO 0.222 APP 0.808 1.30 8034 30 A 0.308 GPR179 0.789 1.30 2301 30 GPR1 0.289 TUBB2A 0.781 1.28 1518 30 TUBB 0.281 LPHN2 0.705 1.26 1045 30 LPH 0.205 PFKP 0.768 1.25 3969 30 PF 0.268 ISL1 0.814 1.23 4990 30 IS 0.314 PROX1 0.776 1.21 4681 30 PRO 0.276 RRBP1 0.705 1.16 9975 30 RRB 0.205 GABRB3 0.703 1.16 0288 30 GABR 0.203 MEIS2 0.709 1.13 1658 30 MEI 0.209 GNG13 0.728 1.09 5088 30 GNG 0.228 LIN7A 0.754 1.08 9638 30 LIN 0.254 GRIA2 0.755 1.02 6466 30 GRI 0.255 HMGN1 0.256 −1.17 6538 30 HMG 0.244 ROM1 0.290 −1.26 0595 30 RO 0.210 RPGRIP1 0.273 −1.55 6331 30 RPGRI 0.227 RS1 0.279 −1.57 7275 30 R 0.221 GNGT1 0.219 −1.58 9959 30 GNG 0.281 RP1 0.255 −1.60 4790 30 R 0.245 GNB1 0.225 −1.61 1631 30 GN 0.275 NRL 0.272 −1.62 8679 30 N 0.228 NR2E3 0.271 −1.64 1338 30 NR2 0.229 CNGA1 0.272 −1.66 4166 30 CNG 0.228 PDE6B 0.248 −1.67 9470 30 PDE 0.252 TULP1 0.222 −1.68 8332 30 TUL 0.278 PRPH2 0.212 −1.69 0732 30 PRP 0.288 SLC24A1 0.280 −1.69 0999 30 SLC24 0.220 PDE6G 0.227 −1.72 7951 30 PDE 0.273 SAG 0.180 −1.74 5533 30 S 0.320 GNAT1 0.217 −1.75 8815 30 GNA 0.283 RCVRN 0.223 −1.82 2401 30 RCV 0.277 PDC 0.186 −1.83 3586 30 P 0.314 RHO 0.180 −1.85 2674 30 R 0.320 cluster no. 31 DE = 58 LHX4 0.834 1.94 0702 31 LH 0.334 SCGN 0.830 1.88 5197 31 SC 0.330 GSG1 0.798 1.78 8603 31 GS 0.298 NEUROD4 0.859 1.76 0730 31 NEURO 0.359 FRMD3 0.823 1.75 3604 31 FRM 0.323 PCP2 0.899 1.74 5442 31 PC 0.399 SCG2 0.863 1.69 1052 31 SC 0.363 SPHKAP 0.803 1.68 8447 31 SPHK 0.303 LPHN2 0.778 1.68 5417 31 LPH 0.278 CABP5 0.752 1.63 6734 31 CAB 0.252 B3GALT2 0.786 1.61 2381 31 B3GAL 0.286 GUCY1A3 0.797 1.57 4051 31 GUCY1 0.297 GNG13 0.855 1.57 2693 31 GNG 0.355 LMO4 0.763 1.54 9801 31 LM 0.263 PTPRZ1 0.720 1.47 1441 31 PTPR 0.220 CDH11 0.701 1.46 3621 31 CDH 0.201 ST18 0.709 1.46 0354 31 ST 0.209 CAR10 0.772 1.45 5466 31 CAR 0.272 CADPS 0.770 1.41 8726 31 CAD 0.270 GNB3 0.830 1.41 6090 31 GN 0.330 BHLHE23 0.705 1.38 4752 31 BHLHE 0.205 SLC24A3 0.721 1.29 4047 31 SLC24 0.221 GRM6 0.754 1.28 4539 31 GR 0.254 NRXN3 0.791 1.26 7068 31 NRX 0.291 LIN7A 0.768 1.25 5294 31 LIN 0.268 RAB3C 0.710 1.25 1581 31 RAB 0.210 PTPRD 0.740 1.23 6538 31 PTP 0.240 ISL1 0.803 1.22 8409 31 IS 0.303 PROX1 0.749 1.21 1165 31 PRO 0.249 FAM184A 0.722 1.20 6153 31 FAM18 0.222 SAMSN1 0.713 1.20 3743 31 SAMS 0.213 VSX2 0.749 1.19 7906 31 VS 0.249 GM4792 0.721 1.14 0953 31 GM47 0.221 GPR179 0.746 1.10 2994 31 GPR1 0.246 GUCY1B3 0.703 1.07 1395 31 GUCY1 0.203 KCNMA1 0.708 1.06 2611 31 KCNM 0.208 CLTB 0.720 1.05 8852 31 CL 0.220 NREP 0.768 1.04 1988 31 NR 0.268 NME1 0.766 1.02 1691 31 NM 0.266 TCF4 0.724 1.01 5121 31 TC 0.224 ROM1 0.282 −1.31 8723 31 RO 0.218 RPGRIP1 0.290 −1.38 7286 31 RPGRI 0.210 RP1 0.260 −1.45 1372 31 R 0.240 TULP1 0.242 −1.47 2154 31 TUL 0.258 PRPH2 0.236 −1.47 3241 31 PRP 0.264 NR2E3 0.283 −1.49 2186 31 NR2 0.217 CNGA1 0.280 −1.52 3041 31 CNG 0.220 SLC24A1 0.291 −1.57 2972 31 SLC24 0.209 PDE6B 0.250 −1.62 5189 31 PDE 0.250 NRL 0.274 −1.63 0936 31 N 0.226 GNB1 0.217 −1.66 5855 31 GN 0.283 RCVRN 0.233 −1.66 8240 31 RCV 0.267 RS1 0.266 −1.70 2551 31 R 0.234 PDE6G 0.228 −1.70 7456 31 PDE 0.272 GNAT1 0.222 −1.71 8310 31 GNA 0.278 SAG 0.184 −1.74 9072 31 S 0.316 RHO 0.185 −1.76 0460 31 R 0.315 PDC 0.188 −1.79 5587 31 P 0.312 cluster no. 32 DE = 81 myAUC myDiff power cluster # IGFN1 0.906 2.609491 0.406 32 VSX1 0.915 2.599423 0.415 32 GM4792 0.916 2.180753 0.416 32 RELN 0.823 2.118713 0.323 32 KCNMA1 0.866 1.893963 0.366 32 GABRR2 0.825 1.851548 0.325 32 GNB3 0.904 1.829577 0.404 32 NDNF 0.827 1.827548 0.327 32 FN1 0.770 1.821386 0.270 32 TMSB10 0.882 1.803877 0.382 32 GNG13 0.903 1.777596 0.403 32 HS3ST4 0.749 1.761498 0.249 32 CDH9 0.746 1.710895 0.246 32 TRNP1 0.838 1.698429 0.338 32 B3GALT2 0.814 1.691321 0.314 32 CADPS 0.842 1.679079 0.342 32 GRM6 0.863 1.668717 0.363 32 PTPRD 0.849 1.654509 0.349 32 LRTM1 0.843 1.632109 0.343 32 CABP2 0.752 1.631417 0.252 32 NME1 0.885 1.576501 0.385 32 GABRA1 0.816 1.567486 0.316 32 GPR179 0.821 1.563361 0.321 32 IGF1 0.781 1.547194 0.281 32 ADCY2 0.719 1.544332 0.219 32 NRXN3 0.849 1.526952 0.349 32 THSD7A 0.786 1.515832 0.286 32 GRIA2 0.827 1.451331 0.327 32 TTYH1 0.863 1.434237 0.363 32 PROX1 0.810 1.418727 0.310 32 GUCY1A3 0.781 1.414072 0.281 32 SULF2 0.722 1.410361 0.222 32 BC030499 0.734 1.321296 0.234 32 SNCB 0.837 1.317248 0.337 32 SH3BGRL 0.722 1.302330 0.222 32 CAR10 0.764 1.297869 0.264 32 FSCN1 0.709 1.288708 0.209 32 4930447C04RIK 0.737 1.286988 0.237 32 ASIC3 0.715 1.284343 0.215 32 TMEM215 0.725 1.282754 0.225 32 TUBB2A 0.789 1.250102 0.289 32 GNAO1 0.855 1.247950 0.355 32 SLC4A10 0.704 1.241247 0.204 32 LPHN2 0.718 1.218956 0.218 32 FRMD3 0.728 1.173764 0.228 32 ATP2B1 0.855 1.171837 0.355 32 PLK5 0.756 1.171749 0.256 32 RIT2 0.715 1.170071 0.215 32 SAMSN1 0.721 1.164145 0.221 32 NAP1L5 0.762 1.144805 0.262 32 PCP4L1 0.730 1.119889 0.230 32 MYO5A 0.707 1.115274 0.207 32 GLS 0.764 1.097304 0.264 32 GUCY1B3 0.711 1.095756 0.211 32 TPI1 0.772 1.082205 0.272 32 MEG3 0.831 1.080701 0.331 32 CAMK2B 0.706 1.058614 0.206 32 MIF 0.772 1.047922 0.272 32 TGFB2 0.737 1.045723 0.237 32 PLCB4 0.724 1.021512 0.224 32 GABRG2 0.706 1.011047 0.206 32 HMGN1 0.260 −1.184526 0.240 32 CST3 0.289 −1.381971 0.211 32 TULP1 0.239 −1.536871 0.261 32 NRL 0.277 −1.561603 0.223 32 RPGRIP1 0.275 −1.597091 0.225 32 SLC24A1 0.276 −1.639280 0.224 32 RP1 0.250 −1.663774 0.250 32 GNB1 0.206 −1.720470 0.294 32 NR2E3 0.269 −1.722247 0.231 32 GNAT1 0.225 −1.727259 0.275 32 CNGA1 0.260 −1.776749 0.240 32 PRPH2 0.200 −1.839337 0.300 32 SAG 0.175 −1.845768 0.325 32 PDE6G 0.212 −1.904791 0.288 32 PDE6B 0.229 −1.905210 0.271 32 RS1 0.254 −1.915177 0.246 32 RCVRN 0.220 −1.923512 0.280 32 GNGT1 0.174 −1.926394 0.326 32 RHO 0.175 −1.927010 0.325 32 PDC 0.173 −1.986239 0.327 32 cluster no. 33 DE = 47 yDiff myAUC m powe r clus t SCGN 0.832 2.3 88592 2 3 3 0.33 VSX1 0.785 2.2 63301 5 3 3 0.28 SCG2 0.843 2.1 13311 3 3 3 0.34 ISL1 0.857 2.0 29040 7 3 3 0.35 CCK 0.706 1.9 93466 6 3 3 0.20 GRM6 0.817 1.8 09452 7 3 3 0.31 GABRA1 0.805 1.7 68523 5 3 3 GA 0.30 RELN 0.729 1.7 64877 9 3 3 0.22 UNC13C 0.726 1.6 81823 6 3 3 UN 0.22 GNG13 0.837 1.6 70562 7 3 3 G 0.33 FRMD3 0.749 1.6 58409 9 3 3 F 0.24 PTPRZ1 0.724 1.6 36544 4 3 3 PT 0.22 CADPS 0.757 1.5 04070 7 3 3 C 0.25 TRPM1 0.848 1.4 72669 8 3 3 T 0.34 BC030499 0.710 1.4 54178 0 3 3 BC03 0.21 SAMSN1 0.710 1.3 71458 0 3 3 SA 0.21 NEUROD4 0.750 1.3 61455 0 3 3 NEU 0.25 PCP4L1 0.711 1.3 31851 1 3 3 PC 0.21 LRTM1 0.737 1.3 30798 7 3 3 L 0.23 APLP2 0.830 1.2 66608 0 3 3 A 0.33 LIN7A 0.740 1.2 23872 0 3 3 L 0.24 GNB3 0.765 1.2 19286 5 3 3 0.26 PROX1 0.716 1.2 04813 6 3 3 P 0.21 GPR179 0.717 1.1 92149 7 3 3 GP 0.21 HMGN3 0.728 1.1 83215 8 3 3 H 0.22 SCG3 0.729 1.1 68200 9 3 3 0.22 MAP4 0.750 1.1 05830 0 3 3 0.25 FAM171B 0.711 1.0 92140 1 3 3 FAM 0.21 PTPRD 0.705 1.0 69875 5 3 3 P 0.20 GNAO1 0.771 1.0 68373 1 3 3 G 0.27 NME1 0.740 1.0 58745 0 3 3 0.24 SLC12A5 0.703 1.0 03819 3 3 3 SLC 0.20 NRXN3 0.702 1.0 00143 2 3 3 N 0.20 TULP1 0.293 −1.0 56098 7 3 3 T 0.20 PRPH2 0.250 −1.2 74321 0 3 3 P 0.25 PDE6B 0.276 −1.2 80609 4 3 3 P 0.22 RCVRN 0.266 −1.2 86723 4 3 3 R 0.23 NRL 0.298 −1.2 87522 2 3 3 0.20 RP1 0.280 −1.2 88988 0 3 3 0.22 NR2E3 0.299 −1.2 92005 1 3 3 N 0.20 PDC 0.223 −1.3 61036 7 3 3 0.27 GNGT1 0.226 −1.3 64027 4 3 3 G 0.27 SAG 0.210 −1.3 86078 0 3 3 0.29 GNAT1 0.246 −1.3 91683 4 3 3 G 0.25 GNB1 0.240 −1.3 95529 0 3 3 0.26 PDE6G 0.251 −1.4 09619 9 3 3 P 0.24 RHO 0.213 −1.4 52949 7 3 3 0.28 myAUC myDiff power cluster # cluster no. 34 DE = 147 GLUL 0.983 3.674486 0.483 34 APOE 0.984 3.656912 0.484 34 RLBP1 0.972 3.488780 0.472 34 CLU 0.954 3.300240 0.454 34 SLC1A3 0.949 3.248626 0.449 34 ACSL3 0.974 3.168933 0.474 34 CYR61 0.778 3.161355 0.278 34 CAR14 0.906 3.093884 0.406 34 SPC25 0.907 3.027510 0.407 34 COL9A1 0.909 2.992981 0.409 34 JUN 0.836 2.955412 0.336 34 DKK3 0.954 2.932319 0.454 34 CP 0.899 2.916545 0.399 34 ID3 0.858 2.906750 0.358 34 DBI 0.935 2.847955 0.435 34 CRYM 0.889 2.732641 0.389 34 HES1 0.812 2.692426 0.312 34 CD9 0.869 2.679822 0.369 34 SPARC 0.943 2.675237 0.443 34 FOS 0.791 2.665697 0.291 34 AQP4 0.855 2.656964 0.355 34 GPR37 0.875 2.652731 0.375 34 DAPL1 0.852 2.601035 0.352 34 KDR 0.861 2.589813 0.361 34 PTN 0.872 2.531457 0.372 34 ZFP36L1 0.773 2.523635 0.273 34 TIMP3 0.839 2.505126 0.339 34 ABCA8A 0.830 2.472855 0.330 34 MFGE8 0.890 2.441779 0.390 34 PRDX6 0.846 2.426776 0.346 34 PDPN 0.813 2.317330 0.313 34 ID2 0.756 2.307350 0.256 34 SIX3OS1 0.835 2.306322 0.335 34 DUSP1 0.707 2.262662 0.207 34 SPON1 0.817 2.237870 0.317 34 MT1 0.747 2.202169 0.247 34 PPAP2B 0.792 2.196871 0.292 34 ESPN 0.807 2.190774 0.307 34 IER2 0.727 2.190246 0.227 34 SAT1 0.786 2.185923 0.286 34 CROT 0.798 2.153557 0.298 34 NUDT4 0.848 2.150174 0.348 34 CRYAB 0.771 2.112165 0.271 34 VIM 0.814 2.088221 0.314 34 EGR1 0.748 2.088219 0.248 34 SOX9 0.740 2.082991 0.240 34 RDH10 0.780 2.082476 0.280 34 CAR2 0.913 2.045093 0.413 34 ID1 0.733 2.038664 0.233 34 GNAI2 0.802 2.032953 0.302 34 VEGFA 0.776 2.021208 0.276 34 NDRG2 0.791 2.017386 0.291 34 CDH2 0.817 2.011985 0.317 34 ENPP2 0.740 2.002079 0.240 34 FLT1 0.768 1.988472 0.268 34 COL23A1 0.777 1.987731 0.277 34 MLC1 0.752 1.962605 0.252 34 FXYD1 0.746 1.938091 0.246 34 TRPM3 0.768 1.927747 0.268 34 COX4I2 0.754 1.915573 0.254 34 FXYD6 0.724 1.911993 0.224 34 SOX2 0.737 1.898436 0.237 34 TSC22D4 0.763 1.895771 0.263 34 E130114P18RIK 0.743 1.893771 0.243 34 PBXIP1 0.739 1.893285 0.239 34 GPM6A 0.846 1.881375 0.346 34 DDR1 0.734 1.861470 0.234 34 ATP1B3 0.750 1.841852 0.250 34 TGFB2 0.795 1.836747 0.295 34 CAV1 0.718 1.808574 0.218 34 CACNG4 0.784 1.804662 0.284 34 UTP14B 0.709 1.801134 0.209 34 IL33 0.706 1.782774 0.206 34 SBSPON 0.710 1.779906 0.210 34 KCNJ10 0.708 1.778244 0.208 34 VCAM1 0.701 1.776161 0.201 34 GAS1 0.706 1.770890 0.206 34 WIPI1 0.754 1.729124 0.254 34 PON2 0.714 1.720217 0.214 34 GPM6B 0.823 1.671461 0.323 34 CNN3 0.739 1.664857 0.239 34 RTN4 0.883 1.661778 0.383 34 ALDOC 0.803 1.656881 0.303 34 JUND 0.742 1.643157 0.242 34 CD63 0.726 1.593887 0.226 34 BSG 0.854 1.587853 0.354 34 SLMAP 0.741 1.575019 0.241 34 TIMP2 0.703 1.573740 0.203 34 TTYH1 0.861 1.556066 0.361 34 ITM2B 0.852 1.552977 0.352 34 SCD2 0.757 1.552154 0.257 34 SYNPR 0.751 1.549654 0.251 34 PAK3 0.718 1.514124 0.218 34 OGFRL1 0.738 1.499757 0.238 34 CTSL 0.787 1.492531 0.287 34 RCN2 0.701 1.447565 0.201 34 CD81 0.765 1.434966 0.265 34 ATP1A1 0.711 1.429682 0.211 34 MARCKS 0.793 1.390002 0.293 34 HTRA1 0.721 1.369298 0.221 34 LAPTM4A 0.737 1.348239 0.237 34 ENO1 0.785 1.330226 0.285 34 PFN2 0.730 1.324261 0.230 34 SLC16A1 0.727 1.315201 0.227 34 PAX6 0.721 1.279765 0.221 34 PRDX1 0.702 1.197453 0.202 34 TCF4 0.738 1.190289 0.238 34 CDKN1B 0.722 1.184339 0.222 34 RTN3 0.743 1.050844 0.243 34 MGARP 0.836 1.038173 0.336 34 TSPAN3 0.718 1.021941 0.218 34 HSP90AA1 0.234 −1.192803 0.266 34 HMGN1 0.196 −1.565337 0.304 34 SLC6A6 0.299 −1.608774 0.201 34 MAP1B 0.292 −1.609128 0.208 34 TMA7 0.272 −1.689161 0.228 34 STX3 0.298 −1.711322 0.202 34 SYT1 0.269 −1.758105 0.231 34 UNC119 0.221 −1.758329 0.279 34 CRX 0.297 −1.766956 0.203 34 CNGB1 0.293 −1.776328 0.207 34 SNAP25 0.257 −1.829279 0.243 34 PDE6A 0.287 −1.834439 0.213 34 FAM57B 0.261 −1.845724 0.239 34 MPP4 0.298 −1.849733 0.202 34 AIPL1 0.277 −1.875251 0.223 34 GNB1 0.184 −1.966538 0.316 34 NRL 0.233 −1.974974 0.267 34 RS1 0.234 −1.987316 0.266 34 SLC24A1 0.241 −1.988035 0.259 34 NEUROD1 0.241 −2.000359 0.259 34 RP1 0.205 −2.033017 0.295 34 CNGA1 0.229 −2.048482 0.271 34 RCVRN 0.190 −2.103059 0.310 34 PDE6B 0.202 −2.104712 0.298 34 ROM1 0.189 −2.109556 0.311 34 NR2E3 0.226 −2.125234 0.274 34 PDE6G 0.178 −2.131050 0.322 34 A930011O12RIK 0.237 −2.131781 0.263 34 TULP1 0.171 −2.188185 0.329 34 GNAT1 0.173 −2.189741 0.327 34 PDC 0.148 −2.206493 0.352 34 PRPH2 0.159 −2.230242 0.341 34 GNGT1 0.140 −2.230657 0.360 34 RHO 0.141 −2.253663 0.359 34 RPGRIP1 0.210 −2.271849 0.290 34 SAG 0.131 −2.316081 0.369 34 cluster no. 35 DE = 164 IGFBP5 0.980 3.971539 0.480 35 IGF2 0.969 3.900102 0.469 35 PTN 0.967 3.682716 0.467 35 S100B 0.935 3.590062 0.435 35 PDGFRA 0.935 3.318071 0.435 35 CST3 0.999 3.249334 0.499 35 APOE 0.969 2.946241 0.469 35 ALDOC 0.949 2.788765 0.449 35 CTGF 0.840 2.723195 0.340 35 ID3 0.891 2.635791 0.391 35 SPARC 0.977 2.633834 0.477 35 MLC1 0.882 2.632886 0.382 35 NTRK2 0.878 2.607959 0.378 35 RGS5 0.854 2.582399 0.354 35 DBI 0.929 2.569035 0.429 35 CNTNAP2 0.890 2.499012 0.390 35 1500015O10RIK 0.759 2.470979 0.259 35 GFAP 0.796 2.454143 0.296 35 ATP1A2 0.882 2.442214 0.382 35 LECT1 0.820 2.435971 0.320 35 CP 0.888 2.423026 0.388 35 PPAP2B 0.858 2.381629 0.358 35 SLC1A3 0.873 2.359476 0.373 35 CD9 0.882 2.341085 0.382 35 FXYD6 0.856 2.288842 0.356 35 SCD2 0.878 2.195817 0.378 35 CLU 0.938 2.194379 0.438 35 CXCL12 0.822 2.156066 0.322 35 SLC4A4 0.809 2.154664 0.309 35 ITM2B 0.960 2.154164 0.460 35 SLC30A10 0.812 2.149123 0.312 35 CLEC18A 0.731 2.137565 0.231 35 TIMP3 0.815 2.122132 0.315 35 CRIM1 0.804 2.079012 0.304 35 SLC6A11 0.799 2.061326 0.299 35 PRDX6 0.828 2.056543 0.328 35 GLUL 0.902 2.039792 0.402 35 IGFBP2 0.762 2.038345 0.262 35 CLDN10 0.759 2.007019 0.259 35 TSC22D4 0.805 1.983938 0.305 35 CRIP1 0.784 1.981687 0.284 35 GPM6B 0.894 1.956617 0.394 35 CD36 0.713 1.930346 0.213 35 MGST1 0.793 1.926971 0.293 35 MGLL 0.813 1.906835 0.313 35 SPON1 0.794 1.903975 0.294 35 MT1 0.762 1.901464 0.262 35 FN1 0.742 1.898765 0.242 35 CGNL1 0.727 1.886294 0.227 35 EPAS1 0.769 1.878394 0.269 35 DDAH1 0.831 1.877818 0.331 35 PAM 0.815 1.876076 0.315 35 VIM 0.816 1.805763 0.316 35 TGFB2 0.824 1.793167 0.324 35 PDLIM3 0.744 1.782440 0.244 35 NPC2 0.807 1.762614 0.307 35 PDPN 0.798 1.757502 0.298 35 CTSL 0.856 1.746857 0.356 35 ID2 0.770 1.744332 0.270 35 LAPTM4A 0.810 1.727350 0.310 35 B2M 0.749 1.719217 0.249 35 FXYD1 0.774 1.684176 0.274 35 MT3 0.756 1.655593 0.256 35 GJA1 0.748 1.648157 0.248 35 1810037I17RIK 0.781 1.644679 0.281 35 LCAT 0.731 1.627679 0.231 35 ID4 0.760 1.626869 0.260 35 CMTM5 0.748 1.625331 0.248 35 MMD2 0.807 1.619960 0.307 35 GPX8 0.733 1.614363 0.233 35 AGT 0.754 1.613099 0.254 35 AP1S2 0.734 1.593596 0.234 35 CTSD 0.755 1.587762 0.255 35 PMP22 0.715 1.581249 0.215 35 CNN3 0.768 1.550185 0.268 35 TRPM3 0.720 1.527377 0.220 35 CD81 0.805 1.514989 0.305 35 TMEM47 0.743 1.510235 0.243 35 SNED1 0.725 1.495801 0.225 35 NDRG2 0.766 1.486505 0.266 35 CDH13 0.708 1.469163 0.208 35 JUN 0.742 1.464296 0.242 35 HES1 0.739 1.463197 0.239 35 SERPINH1 0.739 1.457804 0.239 35 QK 0.771 1.444155 0.271 35 BCAN 0.731 1.443889 0.231 35 ANXA5 0.723 1.441585 0.223 35 ABHD4 0.735 1.440876 0.235 35 PAX8 0.704 1.424204 0.204 35 PLA2G16 0.703 1.398253 0.203 35 6330403K07RIK 0.718 1.387964 0.218 35 RCN1 0.711 1.387198 0.211 35 FBXO2 0.723 1.385921 0.223 35 CRYAB 0.713 1.384143 0.213 35 ITGB1 0.743 1.382103 0.243 35 MAP4K4 0.740 1.374146 0.240 35 METRN 0.721 1.367026 0.221 35 CTNNBIP1 0.730 1.364700 0.230 35 ATP1A1 0.763 1.364599 0.263 35 CNTN1 0.742 1.359653 0.242 35 APPL2 0.720 1.347765 0.220 35 TCEAL3 0.756 1.330603 0.256 35 NFIA 0.705 1.316319 0.205 35 MYO6 0.743 1.310000 0.243 35 SOX2 0.709 1.306380 0.209 35 LSAMP 0.731 1.294332 0.231 35 BTBD3 0.701 1.285695 0.201 35 NFIB 0.726 1.284242 0.226 35 SPARCL1 0.774 1.275405 0.274 35 CD63 0.712 1.268344 0.212 35 TSPAN3 0.826 1.263679 0.326 35 SOX9 0.725 1.263136 0.225 35 SYT11 0.710 1.252546 0.210 35 DKK3 0.819 1.250533 0.319 35 ADD3 0.761 1.231412 0.261 35 OGFRL1 0.710 1.229288 0.210 35 TES 0.701 1.187409 0.201 35 DAD1 0.715 1.143170 0.215 35 CDH2 0.744 1.142469 0.244 35 APP 0.767 1.135626 0.267 35 GNAS 0.806 1.122998 0.306 35 BSG 0.772 1.113302 0.272 35 PSAP 0.756 1.094708 0.256 35 LMAN1 0.753 1.089473 0.253 35 CRIP2 0.718 1.082840 0.218 35 LAMP1 0.751 1.065592 0.251 35 LAMP2 0.715 1.045180 0.215 35 SORBS2 0.703 1.035769 0.203 35 SIX3 0.733 1.025975 0.233 35 SEPT2 0.722 1.024609 0.222 35 PAK3 0.703 1.016054 0.203 35 LRPAP1 0.709 1.015462 0.209 35 D4WSU53E 0.293 −1.205302 0.207 35 HSP90AA1 0.233 −1.301165 0.267 35 HMGN1 0.214 −1.515951 0.286 35 UNC119 0.265 −1.543727 0.235 35 TMA7 0.282 −1.609110 0.218 35 RS1 0.258 −1.739989 0.242 35 EPB4.1 0.294 −1.784873 0.206 35 ROM1 0.228 −1.795256 0.272 35 SNAP25 0.269 −1.797526 0.231 35 A930011O12RIK 0.269 −1.804349 0.231 35 RP1 0.237 −1.805896 0.263 35 NRL 0.269 −1.838217 0.231 35 NR2E3 0.251 −1.864726 0.249 35 GNB1 0.199 −1.937908 0.301 35 PRPH2 0.189 −1.965544 0.311 35 CNGA1 0.249 −1.979693 0.251 35 NEUROD1 0.257 −1.983968 0.243 35 CNGB1 0.290 −1.997218 0.210 35 RCVRN 0.209 −2.010392 0.291 35 RPGRIP1 0.236 −2.027461 0.264 35 SYT1 0.262 −2.027895 0.238 35 GNAT1 0.203 −2.043737 0.297 35 PDE6A 0.291 −2.060641 0.209 35 TULP1 0.203 −2.069090 0.297 35 FAM57B 0.254 −2.158818 0.246 35 PDE6B 0.206 −2.203795 0.294 35 PDE6G 0.188 −2.230095 0.312 35 SLC24A1 0.245 −2.235394 0.255 35 PDC 0.159 −2.252119 0.341 35 GNGT1 0.151 −2.277344 0.349 35 RHO 0.143 −2.360853 0.357 35 SAG 0.138 −2.476095 0.362 35 cluster no. 36 DE = 153 OPTC 0.947 4.425130 0.447 36 CRHBP 0.964 3.776445 0.464 36 ATP1A2 0.951 3.648260 0.451 36 COL9A1 0.976 3.554007 0.476 36 PTGDS 0.915 3.501014 0.415 36 COL18A1 0.946 3.487830 0.446 36 GJA1 0.923 3.420054 0.423 36 FBLN1 0.906 3.182397 0.406 36 IGFBP2 0.885 3.142612 0.385 36 PTN 0.915 3.008914 0.415 36 PENK 0.787 2.989587 0.287 36 CP 0.950 2.984993 0.450 36 FBN2 0.911 2.956232 0.411 36 DAPL1 0.863 2.902905 0.363 36 SNED1 0.879 2.890684 0.379 36 FSTL1 0.908 2.867043 0.408 36 APOE 0.978 2.824762 0.478 36 PVRL3 0.899 2.796596 0.399 36 SPARC 0.956 2.740817 0.456 36 FBN1 0.858 2.736953 0.358 36 TIMP3 0.894 2.725876 0.394 36 ATP1B3 0.887 2.707483 0.387 36 COL23A1 0.899 2.618279 0.399 36 DKK3 0.960 2.573613 0.460 36 RELN 0.859 2.549885 0.359 36 TSC22D1 0.901 2.516971 0.401 36 APP 0.951 2.481702 0.451 36 MFAP4 0.829 2.416559 0.329 36 NTRK2 0.858 2.412425 0.358 36 MEST 0.869 2.407366 0.369 36 LTBP1 0.846 2.364761 0.346 36 VCAN 0.805 2.364323 0.305 36 OGN 0.794 2.342607 0.294 36 FAM129A 0.805 2.301763 0.305 36 ALDH1A1 0.771 2.278916 0.271 36 COL9A2 0.808 2.241696 0.308 36 IQGAP2 0.797 2.216483 0.297 36 NBL1 0.810 2.211997 0.310 36 MFAP2 0.807 2.209952 0.307 36 IGFBP7 0.829 2.206748 0.329 36 MDK 0.795 2.178341 0.295 36 COL2A1 0.792 2.165488 0.292 36 ZIC1 0.775 2.152048 0.275 36 TMPRSS11E 0.747 2.138906 0.247 36 RHOJ 0.813 2.116804 0.313 36 TRPM3 0.813 2.116794 0.313 36 COL9A3 0.788 2.116159 0.288 36 NUDT4 0.864 2.107740 0.364 36 FMOD 0.776 2.038997 0.276 36 BMP4 0.764 2.005755 0.264 36 SFRP1 0.775 2.003735 0.275 36 SLC6A13 0.740 1.996986 0.240 36 SLC13A4 0.759 1.992519 0.259 36 WFDC1 0.745 1.992328 0.245 36 CTSL 0.889 1.973272 0.389 36 SERPINH1 0.797 1.970538 0.297 36 LTBP3 0.776 1.954298 0.276 36 PKP4 0.778 1.935166 0.278 36 CCND2 0.733 1.887738 0.233 36 HTRA1 0.778 1.884120 0.278 36 MGST1 0.756 1.883879 0.256 36 FOLR1 0.750 1.882648 0.250 36 COL4A5 0.756 1.862932 0.256 36 CPQ 0.756 1.838248 0.256 36 GAS1 0.744 1.835410 0.244 36 CTSD 0.841 1.824145 0.341 36 OCIAD2 0.741 1.818916 0.241 36 LIPA 0.746 1.818661 0.246 36 ZIC4 0.711 1.807990 0.211 36 LAPTM4A 0.849 1.799329 0.349 36 SGK1 0.742 1.797747 0.242 36 B3GALTL 0.760 1.785010 0.260 36 OLFML2A 0.723 1.760141 0.223 36 CD63 0.761 1.734796 0.261 36 TGFB2 0.798 1.720278 0.298 36 CGN 0.735 1.702379 0.235 36 BMP2 0.729 1.701840 0.229 36 LRP1 0.733 1.697547 0.233 36 SDC2 0.757 1.685581 0.257 36 TKT 0.792 1.652767 0.292 36 GLDC 0.725 1.644414 0.225 36 CLDN19 0.741 1.636605 0.241 36 TNFRSF21 0.714 1.626433 0.214 36 COL11A1 0.723 1.621136 0.223 36 TENM4 0.743 1.620626 0.243 36 NFIB 0.761 1.612994 0.261 36 VIM 0.779 1.590580 0.279 36 GNG11 0.717 1.589828 0.217 36 CTSH 0.716 1.586077 0.216 36 CNTN1 0.733 1.583022 0.233 36 HES1 0.757 1.576002 0.257 36 SHISA2 0.736 1.573728 0.236 36 MAB21L2 0.752 1.549083 0.252 36 DEFB9 0.706 1.541091 0.206 36 ILDR2 0.709 1.510602 0.209 36 GPX8 0.716 1.484254 0.216 36 PAM 0.736 1.479638 0.236 36 ABI3BP 0.711 1.477928 0.211 36 CD59A 0.728 1.450541 0.228 36 PODXL2 0.765 1.434651 0.265 36 SLC41A1 0.710 1.434087 0.210 36 CD81 0.780 1.424905 0.280 36 CLU 0.795 1.422895 0.295 36 SLC6A6 0.827 1.411126 0.327 36 PAX6 0.752 1.379180 0.252 36 MT-ND6 0.709 1.365749 0.209 36 MT-ND5 0.839 1.364084 0.339 36 PLXNB2 0.701 1.363449 0.201 36 FLRT1 0.703 1.311944 0.203 36 TMEM176B 0.705 1.288783 0.205 36 SDC4 0.741 1.282822 0.241 36 BSG 0.792 1.276199 0.292 36 GM26924 0.759 1.260216 0.259 36 MT-ND2 0.832 1.231683 0.332 36 RRBP1 0.721 1.223343 0.221 36 SLC2A1 0.725 1.220867 0.225 36 CAR14 0.716 1.170677 0.216 36 CD47 0.717 1.167718 0.217 36 PDIA3 0.727 1.157075 0.227 36 GLUL 0.810 1.149020 0.310 36 RCN2 0.716 1.108386 0.216 36 MT-ND4 0.810 1.009844 0.310 36 SYT1 0.291 −1.338995 0.209 36 HSP90AA1 0.202 −1.484852 0.298 36 RS1 0.240 −1.616647 0.260 36 CNGA1 0.263 −1.629758 0.237 36 SNAP25 0.279 −1.656418 0.221 36 HMGN1 0.195 −1.672425 0.305 36 PDE6A 0.293 −1.773595 0.207 36 GNB1 0.208 −1.790238 0.292 36 SLC24A1 0.246 −1.800033 0.254 36 AIPL1 0.285 −1.800568 0.215 36 UNC119 0.225 −1.801700 0.275 36 A930011O12RIK 0.250 −1.828064 0.250 36 ROM1 0.211 −1.886096 0.289 36 NEUROD1 0.245 −1.893158 0.255 36 FAM57B 0.258 −1.960973 0.242 36 NR2E3 0.238 −1.986178 0.262 36 PDE6B 0.210 −2.023997 0.290 36 MGARP 0.241 −2.025761 0.259 36 RPGRIP1 0.225 −2.056657 0.275 36 CNGB1 0.284 −2.060958 0.216 36 NRL 0.235 −2.076837 0.265 36 TULP1 0.187 −2.098105 0.313 36 RP1 0.204 −2.140954 0.296 36 GNGT1 0.151 −2.144535 0.349 36 RCVRN 0.203 −2.146519 0.297 36 PDC 0.153 −2.195983 0.347 36 RHO 0.143 −2.197936 0.357 36 PDE6G 0.185 −2.223749 0.315 36 GNAT1 0.181 −2.279163 0.319 36 SAG 0.133 −2.287358 0.367 36 PRPH2 0.165 −2.298866 0.335 36 cluster no. 37 DE = 236 IGFBP7 0.980 3.838996 0.480 37 CLDN5 0.944 3.452232 0.444 37 RGS5 0.778 3.413786 0.278 37 PTPRB 0.938 3.322368 0.438 37 SPARCL1 0.977 3.260195 0.477 37 SPARC 0.985 3.222677 0.485 37 ITM2A 0.928 3.082648 0.428 37 COL4A1 0.923 3.047394 0.423 37 ELTD1 0.934 3.005777 0.434 37 LY6C1 0.843 2.932233 0.343 37 CTLA2A 0.883 2.913169 0.383 37 PLTP 0.880 2.911192 0.380 37 FLT1 0.945 2.907156 0.445 37 FN1 0.895 2.874017 0.395 37 CD93 0.896 2.763199 0.396 37 RAMP2 0.900 2.687166 0.400 37 BSG 0.959 2.670912 0.459 37 SEPP1 0.867 2.663650 0.367 37 GPR116 0.888 2.662459 0.388 37 FAM101B 0.869 2.611442 0.369 37 MGP 0.747 2.598253 0.247 37 COL4A2 0.884 2.569211 0.384 37 EGFL7 0.861 2.554202 0.361 37 SLCO1A4 0.819 2.547434 0.319 37 TMSB4X 0.958 2.538077 0.458 37 LY6E 0.880 2.518953 0.380 37 SPOCK2 0.887 2.484721 0.387 37 GNG11 0.852 2.460344 0.352 37 SLC7A5 0.832 2.450158 0.332 37 CD34 0.849 2.334600 0.349 37 VWA1 0.836 2.320906 0.336 37 ITGB1 0.848 2.317870 0.348 37 ABCB1A 0.837 2.296619 0.337 37 TM4SF1 0.819 2.273045 0.319 37 PECAM1 0.833 2.249158 0.333 37 LAMA4 0.840 2.246115 0.340 37 CDH5 0.843 2.239309 0.343 37 ETS1 0.824 2.194360 0.324 37 SLCO1C1 0.775 2.175053 0.275 37 SERPINH1 0.825 2.169857 0.325 37 ESAM 0.825 2.149808 0.325 37 SLC16A1 0.835 2.128338 0.335 37 AU021092 0.815 2.116002 0.315 37 SLC2A1 0.871 2.108619 0.371 37 KLF2 0.782 2.108125 0.282 37 NRP1 0.794 2.092760 0.294 37 IFITM3 0.800 2.075435 0.300 37 MFSD2A 0.771 2.062993 0.271 37 ENG 0.803 2.050977 0.303 37 LAMB1 0.794 2.044396 0.294 37 GNAI2 0.858 2.034857 0.358 37 CALD1 0.771 2.033018 0.271 37 APOD 0.731 2.014340 0.231 37 B2M 0.807 2.012573 0.307 37 TPM4 0.812 2.011884 0.312 37 TSC22D1 0.865 1.988874 0.365 37 NID1 0.786 1.988835 0.286 37 AHNAK 0.770 1.972169 0.270 37 MYL12A 0.799 1.968519 0.299 37 HTRA3 0.785 1.966620 0.285 37 KDR 0.851 1.957857 0.351 37 VIM 0.825 1.918437 0.325 37 MYH9 0.792 1.914794 0.292 37 ECE1 0.810 1.899870 0.310 37 EPAS1 0.790 1.873475 0.290 37 LY6A 0.714 1.841976 0.214 37 FOXQ1 0.774 1.840602 0.274 37 TEK 0.756 1.838929 0.256 37 NES 0.766 1.837284 0.266 37 ECSCR 0.750 1.827206 0.250 37 PALMD 0.770 1.814667 0.270 37 SLC7A1 0.757 1.765044 0.257 37 ACTB 0.956 1.764859 0.456 37 RGCC 0.731 1.760596 0.231 37 MSN 0.775 1.756457 0.275 37 PTRF 0.750 1.756409 0.250 37 ANXA3 0.767 1.756155 0.267 37 BC028528 0.764 1.746908 0.264 37 VWF 0.738 1.729667 0.238 37 SLC9A3R2 0.747 1.721684 0.247 37 FZD6 0.758 1.719270 0.258 37 ANXA2 0.762 1.715881 0.262 37 SLC39A10 0.752 1.715856 0.252 37 TIE1 0.748 1.715698 0.248 37 PPIC 0.754 1.692879 0.254 37 KITL 0.723 1.688131 0.223 37 APLNR 0.730 1.686510 0.230 37 PLXND1 0.731 1.679477 0.231 37 SRGN 0.750 1.678497 0.250 37 CRIP2 0.780 1.677601 0.280 37 SPTBN1 0.865 1.671355 0.365 37 RRBP1 0.798 1.669390 0.298 37 SLC39A8 0.726 1.665669 0.226 37 LTBP4 0.715 1.659100 0.215 37 ARPC1B 0.754 1.646160 0.254 37 CSRP2 0.769 1.644461 0.269 37 FLI1 0.748 1.643560 0.248 37 AGRN 0.769 1.641418 0.269 37 ARL4A 0.765 1.635757 0.265 37 TCF4 0.826 1.630606 0.326 37 CLEC14A 0.724 1.627629 0.224 37 RASIP1 0.742 1.626477 0.242 37 APP 0.858 1.625496 0.358 37 CTNNB1 0.815 1.624392 0.315 37 ARHGAP29 0.757 1.621671 0.257 37 RHOB 0.765 1.620359 0.265 37 MYO1B 0.744 1.616759 0.244 37 KANK3 0.738 1.614200 0.238 37 ITGA1 0.739 1.600712 0.239 37 UACA 0.745 1.596853 0.245 37 CDKN1A 0.737 1.596169 0.237 37 NFKBIA 0.767 1.588506 0.267 37 LMO2 0.739 1.587364 0.239 37 ABLIM1 0.817 1.586307 0.317 37 TPM3-RS7 0.753 1.572490 0.253 37 CTSH 0.736 1.560486 0.236 37 ID3 0.798 1.551172 0.298 37 SLC3A2 0.803 1.550705 0.303 37 ITGA6 0.721 1.549646 0.221 37 ABCG2 0.719 1.534372 0.219 37 EMCN 0.734 1.531817 0.234 37 TMEM252 0.712 1.530900 0.212 37 PTPRG 0.737 1.520704 0.237 37 TAGLN2 0.736 1.519652 0.236 37 S1PR1 0.730 1.512398 0.230 37 SDPR 0.706 1.511013 0.206 37 UTRN 0.727 1.510283 0.227 37 SLC40A1 0.725 1.509780 0.225 37 ID1 0.737 1.507196 0.237 37 CD200 0.755 1.505153 0.255 37 EOGT 0.710 1.504481 0.210 37 PLS3 0.716 1.490015 0.216 37 ATOX1 0.781 1.479614 0.281 37 HSPG2 0.709 1.475721 0.209 37 CGNL1 0.724 1.470055 0.224 37 RHOC 0.718 1.454245 0.218 37 ADAM10 0.752 1.454056 0.252 37 CYB5R3 0.744 1.446513 0.244 37 GIMAP6 0.708 1.440910 0.208 37 LAPTM4A 0.788 1.437107 0.288 37 ZFP36L1 0.757 1.431819 0.257 37 FOXP1 0.728 1.428272 0.228 37 GNB4 0.709 1.426711 0.209 37 LRRC58 0.804 1.426417 0.304 37 WWTR1 0.733 1.425046 0.233 37 LSR 0.717 1.424805 0.217 37 REEP3 0.734 1.421046 0.234 37 CNN2 0.719 1.419514 0.219 37 ANXA5 0.720 1.413657 0.220 37 RHOJ 0.724 1.411383 0.224 37 H2-D1 0.720 1.410003 0.220 37 CLIC4 0.725 1.395593 0.225 37 PFN1 0.761 1.389536 0.261 37 ACTN4 0.759 1.381403 0.259 37 MYO10 0.759 1.373926 0.259 37 ROBO4 0.704 1.372148 0.204 37 TMSB10 0.793 1.367258 0.293 37 CLIC1 0.710 1.356832 0.210 37 ABHD2 0.706 1.345547 0.206 37 PTBP3 0.704 1.338826 0.204 37 LEF1 0.706 1.336777 0.206 37 LAMC1 0.704 1.334944 0.204 37 S100A13 0.702 1.331773 0.202 37 RBMS1 0.704 1.324417 0.204 37 GPCPD1 0.736 1.311359 0.236 37 RALB 0.706 1.301303 0.206 37 TPM3 0.740 1.300676 0.240 37 LIMCH1 0.727 1.300556 0.227 37 QK 0.738 1.296033 0.238 37 MAOA 0.703 1.294644 0.203 37 LRP8 0.711 1.293956 0.211 37 NFIB 0.713 1.286120 0.213 37 FERMT2 0.723 1.282462 0.223 37 SERINC3 0.766 1.277661 0.266 37 TPM1 0.733 1.268704 0.233 37 OSTF1 0.712 1.264445 0.212 37 PODXL 0.738 1.258107 0.238 37 DOCK9 0.706 1.254311 0.206 37 PPFIBP1 0.702 1.247757 0.202 37 SELM 0.718 1.243887 0.218 37 IQGAP1 0.718 1.237155 0.218 37 NOTCH1 0.701 1.224235 0.201 37 WASF2 0.701 1.195270 0.201 37 KLF6 0.703 1.182019 0.203 37 RAC1 0.723 1.178323 0.223 37 HES1 0.708 1.178252 0.208 37 SYNM 0.715 1.159417 0.215 37 HIP1 0.712 1.133942 0.212 37 ARPC3 0.705 1.129207 0.205 37 GPX1 0.718 1.126453 0.218 37 TNFAIP1 0.702 1.126067 0.202 37 ACTN1 0.703 1.105354 0.203 37 MYH10 0.715 1.105079 0.215 37 CAPNS1 0.712 1.100011 0.212 37 HSP90AB1 0.823 1.063223 0.323 37 ITM2B 0.775 1.046377 0.275 37 CTNNA1 0.735 1.045557 0.235 37 ARPC5 0.714 1.035917 0.214 37 ARPC2 0.741 1.002383 0.241 37 GNB2 0.709 1.000695 0.209 37 CD2AP 0.705 1.000147 0.205 37 GNB1 0.250 −1.474782 0.250 37 TMA7 0.293 −1.657448 0.207 37 HSP90AA1 0.188 −1.688760 0.312 37 ANP32E 0.287 −1.782614 0.213 37 HMGN1 0.187 −1.810023 0.313 37 EPB4.1 0.297 −1.825915 0.203 37 CNGA1 0.245 −1.839320 0.255 37 CRX 0.298 −1.856625 0.202 37 CKB 0.258 −1.875027 0.242 37 SNAP25 0.270 −1.886785 0.230 37 PDE6A 0.291 −1.892818 0.209 37 NEUROD1 0.252 −1.945246 0.248 37 SYT1 0.264 −1.950146 0.236 37 AIPL1 0.279 −1.961332 0.221 37 UNC119 0.213 −1.984213 0.287 37 FAM57B 0.260 −1.996296 0.240 37 RS1 0.242 −1.998138 0.258 37 MGARP 0.241 −2.018440 0.259 37 ROM1 0.207 −2.054687 0.293 37 RCVRN 0.204 −2.079733 0.296 37 GNAT1 0.187 −2.113967 0.313 37 NRL 0.235 −2.122317 0.265 37 SLC24A1 0.248 −2.125249 0.252 37 RP1 0.211 −2.136068 0.289 37 PRPH2 0.177 −2.140244 0.323 37 PDE6B 0.206 −2.170048 0.294 37 NR2E3 0.229 −2.230401 0.271 37 PDE6G 0.181 −2.259370 0.319 37 TULP1 0.177 −2.260649 0.323 37 PDC 0.154 −2.296981 0.346 37 RHO 0.144 −2.311761 0.356 37 A930011O12RIK 0.240 −2.318021 0.260 37 GNGT1 0.142 −2.329702 0.358 37 SAG 0.136 −2.357981 0.364 37 RPGRIP1 0.210 −2.484476 0.290 37 cluster no. 38 DE = 147 RGS5 0.992 5.501167 0.492 38 MGP 0.992 4.465241 0.492 38 IGFBP7 0.966 4.035969 0.466 38 COL4A1 0.974 3.632199 0.474 38 CALD1 0.989 3.427224 0.489 38 COL4A2 0.925 3.164541 0.425 38 ATP1A2 0.916 3.153645 0.416 38 SERPINE2 0.867 3.078251 0.367 38 ASPN 0.904 3.066492 0.404 38 KCNJ8 0.801 2.949732 0.301 38 ABCC9 0.825 2.914127 0.325 38 ITGA1 0.880 2.901163 0.380 38 NID1 0.887 2.865895 0.387 38 MYL9 0.848 2.784330 0.348 38 SPARCL1 0.921 2.771803 0.421 38 HIGD1B 0.841 2.751780 0.341 38 FSTL1 0.836 2.746793 0.336 38 ITGB1 0.843 2.690748 0.343 38 ITIH5 0.713 2.661303 0.213 38 GNG11 0.837 2.649734 0.337 38 COL1A2 0.814 2.596983 0.314 38 COL3A1 0.785 2.565582 0.285 38 PDGFRB 0.856 2.494842 0.356 38 GJC1 0.829 2.453495 0.329 38 TM4SF1 0.768 2.425629 0.268 38 CRIP1 0.720 2.420014 0.220 38 IFITM3 0.799 2.413464 0.299 38 CSPG4 0.761 2.403481 0.261 38 SPARC 0.940 2.383060 0.440 38 MYO1B 0.795 2.250938 0.295 38 MYL12A 0.804 2.246027 0.304 38 SERPINH1 0.794 2.240935 0.294 38 MCAM 0.768 2.235239 0.268 38 ART3 0.769 2.225034 0.269 38 CASQ2 0.730 2.198628 0.230 38 LAMA4 0.752 2.197344 0.252 38 LAMB1 0.765 2.179149 0.265 38 TPM4 0.786 2.173681 0.286 38 CD248 0.769 2.172865 0.269 38 TPM1 0.728 2.168649 0.228 38 LAMC1 0.806 2.152352 0.306 38 ETS1 0.744 2.113024 0.244 38 GJA4 0.714 2.090454 0.214 38 TIMP3 0.753 2.075556 0.253 38 CFH 0.713 2.068239 0.213 38 EDNRA 0.777 2.041461 0.277 38 NDUFA4L2 0.790 2.032572 0.290 38 SEPT7 0.903 2.026055 0.403 38 EBF1 0.805 2.024674 0.305 38 PTRF 0.720 2.024501 0.220 38 NOTCH3 0.722 2.014656 0.222 38 SEPT11 0.798 2.003902 0.298 38 PLAT 0.750 2.002567 0.250 38 S1PR3 0.755 1.999823 0.255 38 UACA 0.729 1.995204 0.229 38 MYH9 0.760 1.981694 0.260 38 RGS4 0.741 1.980531 0.241 38 FLNA 0.708 1.979751 0.208 38 NAALAD2 0.753 1.962642 0.253 38 S100A11 0.743 1.951513 0.243 38 NRP1 0.785 1.946284 0.285 38 SEPT4 0.805 1.932384 0.305 38 BGN 0.745 1.895552 0.245 38 PPIC 0.751 1.881210 0.251 38 PCDH18 0.743 1.866156 0.243 38 MAGED2 0.752 1.849301 0.252 38 CNN2 0.721 1.848057 0.221 38 NBL1 0.737 1.837023 0.237 38 MARCKS 0.837 1.808142 0.337 38 VIM 0.745 1.769597 0.245 38 ARHGDIB 0.705 1.769381 0.205 38 B2M 0.735 1.764019 0.235 38 ADAP2 0.706 1.740003 0.206 38 EPAS1 0.760 1.738220 0.260 38 NR2F2 0.741 1.729772 0.241 38 UTRN 0.712 1.709004 0.212 38 ID3 0.737 1.706232 0.237 38 GUCY1A3 0.798 1.705109 0.298 38 ACTB 0.929 1.685265 0.429 38 LAPTM4A 0.815 1.676642 0.315 38 RHOB 0.727 1.667873 0.227 38 RBMS1 0.708 1.644134 0.208 38 LRRC58 0.827 1.640398 0.327 38 MEF2C 0.712 1.640375 0.212 38 CCDC80 0.713 1.628830 0.213 38 ANXA5 0.715 1.584120 0.215 38 ITM2B 0.851 1.582958 0.351 38 FERMT2 0.705 1.565111 0.205 38 CD63 0.718 1.561257 0.218 38 MFGE8 0.772 1.548767 0.272 38 WLS 0.702 1.535632 0.202 38 MPRIP 0.725 1.530097 0.225 38 SERINC3 0.738 1.514550 0.238 38 SLC12A2 0.722 1.511126 0.222 38 LHFP 0.701 1.509888 0.201 38 GINM1 0.703 1.495549 0.203 38 CD81 0.819 1.485171 0.319 38 VTN 0.735 1.473185 0.235 38 APP 0.793 1.469747 0.293 38 RAC1 0.714 1.426087 0.214 38 TNFAIP1 0.705 1.405605 0.205 38 OAZ2 0.706 1.349629 0.206 38 NREP 0.759 1.298044 0.259 38 PTEN 0.719 1.252699 0.219 38 TMSB4X 0.772 1.138718 0.272 38 SPTBN1 0.716 1.048742 0.216 38 LAMP1 0.737 1.039290 0.237 38 D4WSU53E 0.298 −1.146991 0.202 38 SNAP25 0.296 −1.457235 0.204 38 HSP90AA1 0.203 −1.509721 0.297 38 MGARP 0.250 −1.523003 0.250 38 NEUROD1 0.277 −1.532473 0.223 38 HMGN1 0.188 −1.744434 0.312 38 SLC24A1 0.267 −1.817590 0.233 38 TMA7 0.271 −1.832750 0.229 38 FAM57B 0.267 −1.841072 0.233 38 SYT1 0.265 −1.845972 0.235 38 CRX 0.292 −1.876962 0.208 38 ELOVL4 0.297 −1.878393 0.203 38 CKB 0.250 −1.883214 0.250 38 UNC119 0.220 −1.904351 0.280 38 NDUFA4 0.220 −1.957810 0.280 38 MPP4 0.295 −1.968298 0.205 38 AIPL1 0.278 −1.972794 0.222 38 EPB4.1 0.279 −2.013098 0.221 38 GNB1 0.192 −2.027415 0.308 38 NR2E3 0.232 −2.098991 0.268 38 1810009A15RIK 0.291 −2.121394 0.209 38 PDE6G 0.188 −2.141490 0.312 38 PDE6A 0.274 −2.163994 0.226 38 NRL 0.233 −2.193383 0.267 38 CNGB1 0.282 −2.220351 0.218 38 RS1 0.227 −2.230808 0.273 38 TULP1 0.179 −2.310206 0.321 38 CNGA1 0.225 −2.318757 0.275 38 RCVRN 0.188 −2.319052 0.312 38 RP1 0.201 −2.341225 0.299 38 RHO 0.153 −2.379167 0.347 38 RPGRIP1 0.215 −2.390692 0.285 38 PDC 0.147 −2.404465 0.353 38 A930011O12RIK 0.231 −2.444744 0.269 38 GNAT1 0.174 −2.450650 0.326 38 SAG 0.140 −2.497791 0.360 38 PDE6B 0.194 −2.533895 0.306 38 PRPH2 0.151 −2.581111 0.349 38 ROM1 0.175 −2.590215 0.325 38 GNGT1 0.133 −2.660261 0.367 38 cluster no. 39 DE = 153 CTSS 0.978 4.653922 0.478 39 HEXB 0.976 4.292110 0.476 39 C1QB 0.970 3.878037 0.470 39 C1QC 0.948 3.834225 0.448 39 APOE 0.962 3.754892 0.462 39 C1QA 0.948 3.723967 0.448 39 CCL4 0.754 3.720710 0.254 39 B2M 0.938 3.647541 0.438 39 CX3CR1 0.903 3.520550 0.403 39 LY86 0.903 3.481497 0.403 39 P2RY12 0.880 3.398210 0.380 39 CCL3 0.791 3.365822 0.291 39 SEPP1 0.916 3.341246 0.416 39 CSF1R 0.895 3.191319 0.395 39 LAPTM5 0.903 3.170011 0.403 39 ZFP36 0.875 3.154473 0.375 39 TYROBP 0.873 3.084486 0.373 39 JUNB 0.862 3.023664 0.362 39 NFKBIA 0.805 3.015364 0.305 39 KLF2 0.729 2.944302 0.229 39 SIGLECH 0.880 2.904145 0.380 39 ATF3 0.751 2.874536 0.251 39 TREM2 0.851 2.847238 0.351 39 JUN 0.901 2.800797 0.401 39 CTSD 0.895 2.785069 0.395 39 RHOB 0.863 2.668613 0.363 39 SGK1 0.791 2.595234 0.291 39 FCER1G 0.820 2.594593 0.320 39 SELPLG 0.791 2.583273 0.291 39 MPEG1 0.806 2.561161 0.306 39 TMSB4X 0.978 2.518332 0.478 39 GPR34 0.776 2.484680 0.276 39 SERPINE2 0.851 2.447607 0.351 39 SPARC 0.906 2.436520 0.406 39 GRN 0.813 2.425319 0.313 39 IER5 0.773 2.410207 0.273 39 NPC2 0.832 2.385903 0.332 39 LGMN 0.952 2.385703 0.452 39 KLF6 0.744 2.379144 0.244 39 LYZ2 0.746 2.374372 0.246 39 EGR1 0.834 2.333774 0.334 39 FCGR3 0.776 2.313824 0.276 39 RGS2 0.803 2.307229 0.303 39 4632428N05RIK 0.768 2.250471 0.268 39 CTSZ 0.821 2.233623 0.321 39 CST3 0.964 2.231930 0.464 39 ITGAM 0.752 2.200036 0.252 39 ACTB 0.956 2.193357 0.456 39 FYB 0.773 2.190362 0.273 39 TGFBR1 0.766 2.176746 0.266 39 KCTD12 0.757 2.169558 0.257 39 UNC93B1 0.746 2.159913 0.246 39 AIF1 0.754 2.148845 0.254 39 CYBA 0.759 2.143158 0.259 39 MAFB 0.725 2.130408 0.225 39 CTSB 0.900 2.106910 0.400 39 H2-D1 0.755 2.100278 0.255 39 DUSP1 0.721 2.084336 0.221 39 RNASE4 0.716 2.084032 0.216 39 SERINC3 0.830 2.075356 0.330 39 PTGS1 0.739 2.071713 0.239 39 FCRLS 0.746 2.055869 0.246 39 UBC 0.834 2.024625 0.334 39 LAIR1 0.737 2.014039 0.237 39 H2-K1 0.719 2.013817 0.219 39 CTSL 0.887 2.003522 0.387 39 LY6E 0.764 2.000438 0.264 39 ITGB5 0.740 1.998945 0.240 39 PSAP 0.854 1.998267 0.354 39 SAT1 0.739 1.997578 0.239 39 LTC4S 0.731 1.992351 0.231 39 ARPC1B 0.736 1.989627 0.236 39 MARCKS 0.877 1.984915 0.377 39 CD53 0.716 1.979296 0.216 39 LRRC58 0.877 1.965416 0.377 39 APBB1IP 0.709 1.956031 0.209 39 BTG2 0.755 1.955286 0.255 39 PLEK 0.711 1.946862 0.211 39 RGS10 0.737 1.924107 0.237 39 IER2 0.721 1.912803 0.221 39 PLXDC2 0.738 1.910001 0.238 39 F11R 0.714 1.890608 0.214 39 IRF8 0.701 1.868279 0.201 39 PLD4 0.731 1.865511 0.231 39 CTSA 0.754 1.835910 0.254 39 FOS 0.723 1.826721 0.223 39 MAF 0.714 1.823466 0.214 39 ITM2B 0.917 1.811843 0.417 39 CD9 0.765 1.806437 0.265 39 IFNGR1 0.742 1.805089 0.242 39 JUND 0.756 1.804582 0.256 39 LPCAT2 0.767 1.791338 0.267 39 CTSH 0.725 1.784451 0.225 39 MERTK 0.706 1.779292 0.206 39 TRF 0.720 1.778704 0.220 39 CD81 0.808 1.768962 0.308 39 CLIC1 0.711 1.731795 0.211 39 RRBP1 0.751 1.708872 0.251 39 GPX1 0.731 1.694199 0.231 39 MSN 0.704 1.650198 0.204 39 CREG1 0.725 1.641235 0.225 39 TPM3-RS7 0.702 1.631400 0.202 39 LAMP2 0.704 1.522471 0.204 39 TIMP2 0.705 1.496183 0.205 39 QK 0.707 1.462699 0.207 39 FTH1 0.853 1.383409 0.353 39 TPM3 0.707 1.355049 0.207 39 LAMP1 0.743 1.274744 0.243 39 RPS9 0.740 1.227865 0.240 39 GM9843 0.764 1.151216 0.264 39 RPL32 0.759 1.139049 0.259 39 RPS26 0.720 1.130727 0.220 39 RPLP1 0.801 1.070725 0.301 39 ANP32A 0.299 −1.167042 0.201 39 HSP90AA1 0.239 −1.203183 0.261 39 LDHA 0.291 −1.203661 0.209 39 PKM 0.295 −1.277540 0.205 39 NDUFA4 0.273 −1.304189 0.227 39 MAP1B 0.294 −1.335660 0.206 39 SYT1 0.285 −1.366615 0.215 39 FAM57B 0.292 −1.413749 0.208 39 NRL 0.279 −1.427076 0.221 39 ROM1 0.235 −1.486101 0.265 39 ANP32E 0.287 −1.504556 0.213 39 TULP1 0.220 −1.539643 0.280 39 SLC25A4 0.273 −1.541158 0.227 39 MGARP 0.256 −1.541918 0.244 39 CPE 0.268 −1.556102 0.232 39 TMA7 0.277 −1.583630 0.223 39 SNAP25 0.275 −1.599132 0.225 39 PDE6G 0.205 −1.660203 0.295 39 RS1 0.254 −1.663552 0.246 39 PRPH2 0.191 −1.751501 0.309 39 RCVRN 0.213 −1.764180 0.287 39 GNAT1 0.205 −1.772742 0.295 39 SLC24A1 0.261 −1.776117 0.239 39 EPB4.1 0.280 −1.821400 0.220 39 PDE6A 0.290 −1.837114 0.210 39 GNGT1 0.172 −1.846910 0.328 39 NEUROD1 0.248 −1.869328 0.252 39 UNC119 0.209 −1.871789 0.291 39 A930011O12RIK 0.254 −1.877904 0.246 39 STX3 0.288 −1.894001 0.212 39 CNGA1 0.243 −1.905085 0.257 39 HMGN1 0.165 −1.905216 0.335 39 NR2E3 0.237 −1.943295 0.263 39 RHO 0.166 −1.984178 0.334 39 GNB1 0.182 −2.006714 0.318 39 PDC 0.163 −2.033191 0.337 39 PDE6B 0.205 −2.102541 0.295 39 RPGRIP1 0.221 −2.105338 0.279 39 SAG 0.154 −2.197799 0.346 39 RP1 0.197 −2.359118 0.303 39

TABLE 7 Cost analysis of Drop-Seq. Cost for Reagents Supplier Catalog # 10,000 cells ($) Microfluidics costs (tubing, syringes, N/A N/A 35.00 droplet generation oil, device fabrication) DropSeq lysis buffer (Ficoll, Tris, N/A N/A 9.35 Sarkosyl, EDTA, DTT) Barcoded microparticles Chemgenes N/A 137.20 Maxima H- Reverse Transcriptase Thermo EP0753 53.10 dNTP mix Clontech 639125 7.00 RNase inhibitor Lucigen 30281-2 3.44 Template switch oligo IDT N/A 6.90 Perfluorooctanol Sigma 370533 10.70 Exonuclease I NEB M0293L 3.46 KAPA Hifi HotStart ReadyMix KAPA BioSystems KK2602 210.00 Nextera XT DNA sample preparation kit Illumina FC-131-1096 120.80 Ampure XP beads Beckman Coulter A63882 37.35 BioAnalyzer High Sensitivity Chips Agilent 5067-4626 9.64 Total cost: $633.94 Cost per cell: $0.06

TABLE 9 Oligonucleotide sequences used in the preparation of Drop-Seq libraries. “B” designates any base but “A”, “J” designates a split-and-pool synthesis round; “N” designates a degenerate base. “*” designates a phosphorothioate linkage. All soluble primers were purchased from Integrated DNA Technolo- gies, and purified by standard desalting except for the Template_Switch_ Oligo, which was purified by ion-exchange-HPLC. Table discloses SEQ ID NOS: 2-16, respectively, in order of appearance. synRNA rCrCrUrArCrArCrCrArCrCrCrUrCrUrUrCrCrCrArUrCrUrNrNrNrNrNr NrNrNrNrNrNrNrNrNrNrNrNrNrNrBrArArArArArArArArArArArArArA rArArArArArArArArArA Barcoded Bead 5′-Bead-Linker-TTTTTTTAAGCAGTGGTATCAACGCAGAGTACGTJJJJJJJJ SeqA JJJJNNNNNNNNTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-3′ Barcoded Bead 5′-Bead-Linker-TTTTTTTAAGCAGTGGTATCAACGCAGAGTACJJJJJJJJJJ SeqB JJNNNNNNNNTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-3′ Template_Switch_ AAGCAGTGGTATCAACGCAGAGTGAATrGrGrG Oligo TSO_PCR AAGCAGTGGTATCAACGCAGAGT P5-TSO_Hybrid AATGATACGGCGACCACCGAGATCTACACGCCTGTCCGCGGAAGCAGTGGTATCAACGC AGAGT*A*C Nextera_N701 CAAGCAGAAGACGGCATACGAGATTCGCCTTAGTCTCGTGGGCTCGG Nextera_N702 CAAGCAGAAGACGGCATACGAGATCTAGTACGGTCTCGTGGGCTCGG Nextera_N703 CAAGCAGAAGACGGCATACGAGATTTCTGCCTGTCTCGTGGGCTCGG Read1 CustomSeqA GCCTGTCCGCGGAAGCAGTGGTATCAACGCAGAGTACGT Read1 CustomSeqB GCCTGTCCGCGGAAGCAGTGGTATCAACGCAGAGTAC P7-TSO_Hybrid CAAGCAGAAGACGGCATACGAGATCGTGATCGGTCTCGGCGGAAGCAGTGGTATCAAC GCAGAGT*A*C TruSeq_F AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATC*T CustSynRNASeq CGGTCTCGGCGGAAGCAGTGGTATCAACGCAGAGTAC UMI_SMARTdT AAGCAGTGGTATCAACGCAGAGTACNNNNNNNNNTTTTTTTTTTTTTTTTTTTTTTTT

TABLE 10 “Out of sample” projection test. For each cluster, the “training” cells were removed from the tSNE plot, and then projected onto the tSNE. The number of cells that successfully project into the embedding, and the number of cells that become inappropriately incorporated into a different cluster were tabulated. Cluster # Cells in # failed to # Wrongly % Wrongly # Cluster project # Projected Assigned Assigned 1 153 153 0 0 0.00 2 271 271 0 0 0.00 3 201 201 0 0 0.00 4 46 46 0 0 0.00 5 63 62 1 0 0.00 6 173 156 17 9 5.20 7 277 272 5 5 1.81 8 115 115 0 0 0.00 9 275 275 0 0 0.00 10 155 153 2 2 1.29 11 165 162 3 3 1.82 12 175 175 0 0 0.00 13 46 40 6 5 10.87 14 89 89 0 0 0.00 15 52 44 8 6 11.54 16 179 179 0 0 0.00 17 284 284 0 0 0.00 18 64 63 1 1 1.56 19 108 107 1 0 0.00 20 206 206 0 0 0.00 21 154 154 0 0 0.00 22 180 180 0 0 0.00 23 183 182 1 1 0.55 24 3712 3417 295 180 4.85 25 1095 1071 24 18 1.64 26 1213 1212 1 0 0.00 27 323 318 5 4 1.24 28 339 330 9 7 2.06 29 332 324 8 6 1.81 30 447 426 21 18 4.03 31 346 340 6 3 0.87 32 235 233 2 2 0.85 33 453 450 3 3 0.66 34 784 784 0 0 0.00 35 27 27 0 0 0.00 36 43 43 0 0 0.00 37 145 139 6 5 3.45 38 30 30 0 0 0.00 39 17 17 0 0 0.00

REFERENCES

-   Andersen, B. B., Korbo, L., and Pakkenberg, B. (1992). A     quantitative study of the human cerebellum with unbiased     stereological techniques. The Journal of comparative neurology 326,     549-560. -   Bar-Joseph, Z., Siegfried, Z., Brandeis, M., Brors, B., Lu, Y.,     Eils, R., Dynlacht, B. D., and Simon, I. (2008). Genome-wide     transcriptional analysis of the human cell cycle identifies genes     differentially regulated in normal and cancer cells. Proceedings of     the National Academy of Sciences of the United States of America     105, 955-960. -   Barres, B. A., Silverstein, B. E., Corey, D. P., and Chun, L. L.     (1988). Immunological, morphological, and electrophysiological     variation among retinal ganglion cells purified by panning. Neuron     1, 791-803. -   Beer, N. R., Wheeler, E. K., Lee-Houghton, L., Watkins, N.,     Nasarabadi, S., Hebert, N., Leung, P., Arnold, D. W., Bailey, C. G.,     and Colston, B. W. (2008). On-chip single-copy real-time     reverse-transcription PCR in isolated picoliter droplets. Analytical     chemistry 80, 1854-1858. -   Berman, G. J., Choi, D. M., Bialek, W., and Shaevitz, J. W. (2014).     Mapping the stereotyped behaviour of freely moving fruit flies.     Journal of the Royal Society, Interface/the Royal Society 11. -   Brennecke, P., Anders, S., Kim, J. K., Kolodziejczyk, A. A., Zhang,     X., Proserpio, V., Baying, B., Benes, V., Teichmann, S. A.,     Marioni, J. C., et al. (2013). Accounting for technical noise in     single-cell RNA-seq experiments. Nature methods 10, 1093-1095. -   Bringer, M. R., Gerdts, C. J., Song, H., Tice, J. D., and     Ismagilov, R. F. (2004). Microfluidic systems for chemical kinetics     that rely on chaotic mixing in droplets. Philosophical transactions     Series A, Mathematical, physical, and engineering sciences 362,     1087-1104. -   Britten, R. J., and Kohne, D. E. (1968). Repeated sequences in DNA.     Hundreds of thousands of copies of DNA sequences have been     incorporated into the genomes of higher organisms. Science 161,     529-540. -   Brouzes, E., Medkova, M., Savenelli, N., Marran, D., Twardowski, M.,     Hutchison, J. B., Rothberg, J. M., Link, D. R., Perrimon, N., and     Samuels, M. L. (2009). Droplet microfluidic technology for     single-cell high-throughput screening. Proceedings of the National     Academy of Sciences of the United States of America 106,     14195-14200. -   Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V.,     Scialdone, A., Theis, F. J., Teichmann, S. A., Marioni, J. C., and     Stegle, O. (2015). Computational analysis of cell-to-cell     heterogeneity in single-cell RNA-sequencing data reveals hidden     subpopulations of cells. Nature biotechnology 33, 155-160. -   Carter-Dawson, L. D., and LaVail, M. M. (1979). Rods and cones in     the mouse retina. I. Structural analysis using light and electron     microscopy. The Journal of comparative neurology 188, 245-262. -   Cheong, H. K., Hwang, E., and Cheong, C. (2012). Rapid preparation     of RNA samples using DNA-affinity chromatography and DNAzyme     methods. Methods in molecular biology 941, 113-121. -   Chung, N. C., and Storey, J. D. (2014). Statistical Significance of     Variables Driving Systematic Variation in High-Dimensional Data.     Bioinformatics. -   Corbo, J. C., Myers, C. A., Lawrence, K. A., Jadhav, A. P., and     Cepko, C. L. (2007). A typology of photoreceptor gene expression     patterns in the mouse. Proceedings of the National Academy of     Sciences of the United States of America 104, 12069-12074. -   Descamps, F. J., Martens, E., Proost, P., Starckx, S., Van den     Steen, P. E., Van Damme, J., and Opdenakker, G. (2005). Gelatinase     B/matrix metalloproteinase-9 provokes cataract by cleaving lens     betaB1 crystallin. FASEB journal: official publication of the     Federation of American Societies for Experimental Biology 19, 29-35. -   Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C.,     Jha, S., Batut, P., Chaisson, M., and Gingeras, T. R. (2013). STAR:     ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21. -   Ester, M., Kriegel, H. P., Sander, J., and Xu, X. (1996). A     density-based algorithm for discovering clusters in large spatial     databases with noise. (Menlo Park, Calif.: AAAI Press). -   Famiglietti, E. V., and Sundquist, S. J. (2010). Development of     excitatory and inhibitory neurotransmitters in transitory     cholinergic neurons, starburst amacrine cells, and GABAergic     amacrine cells of rabbit retina, with implications for previsual and     visual development of retinal ganglion cells. Visual neuroscience     27, 19-42. -   Feigenspan, A., Teubner, B., Willecke, K., and Weiler, R. (2001).     Expression of neuronal connexin36 in AII amacrine cells of the     mammalian retina. The Journal of neuroscience: the official journal     of the Society for Neuroscience 21, 230-239. -   Grun, D., Kester, L., and van Oudenaarden, A. (2014). Validation of     noise models for single-cell transcriptomics. Nature methods 11,     637-640. -   Guo, M. T., Rotem, A., Heyman, J. A., and Weitz, D. A. (2012).     Droplet microfluidics for high-throughput biological assays. Lab on     a chip 12, 2146-2155. -   Hashimshony, T., Wagner, F., Sher, N., and Yanai, I. (2012).     CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification.     Cell reports 2, 666-673. -   Hattar, S., Liao, H. W., Takao, M., Berson, D. M., and Yau, K. W.     (2002). Melanopsin-containing retinal ganglion cells: architecture,     projections, and intrinsic photosensitivity. Science 295, 1065-1070. -   Haverkamp, S., and Wassle, H. (2004). Characterization of an     amacrine cell type of the mammalian retina immunoreactive for     vesicular glutamate transporter 3. The Journal of comparative     neurology 468, 251-263. -   Hindson, B. J., Ness, K. D., Masquelier, D. A., Belgrader, P.,     Heredia, N. J., Makarewicz, A. J., Bright, I. J., Lucero, M. Y.,     Hiddessen, A. L., Legler, T. C., et al. (2011). High-throughput     droplet digital PCR system for absolute quantitation of DNA copy     number. Analytical chemistry 83, 8604-8610. -   Hoon, M., Okawa, H., Della Santina, L., and Wong, R. O. (2014).     Functional architecture of the retina: development and disease.     Progress in retinal and eye research 42, 44-84. -   Islam, S., Kjallquist, U., Moliner, A., Zajac, P., Fan, J. B.,     Lonnerberg, P., and Linnarsson, S. (2012). Highly multiplexed and     strand-specific single-cell RNA 5′ end sequencing. Nature protocols     7, 813-828. -   Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper,     M., Lonnerberg, P., and Linnarsson, S. (2014). Quantitative     single-cell RNA-seq with unique molecular identifiers. Nature     methods 11, 163-166. -   Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul,     F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et     al. (2014). Massively parallel single-cell RNA-seq for marker-free     decomposition of tissues into cell types. Science 343, 776-779. -   Jarosz, D. F., Brown, J. C., Walker, G. A., Datta, M. S., Ung, W.     L., Lancaster, A. K., Rotem, A., Chang, A., Newby, G. A., Weitz, D.     A., et al. (2014). Cross-kingdom chemical communication drives a     heritable, mutually beneficial prion-based transformation of     metabolism. Cell 158, 1083-1093. -   Jeon, C. J., Strettoi, E., and Masland, R. H. (1998). The major cell     populations of the mouse retina. The Journal of neuroscience: the     official journal of the Society for Neuroscience 18, 8936-8946. -   Kadonaga, J. T. (1991). Purification of sequence-specific binding     proteins by DNA affinity chromatography. Methods in enzymology 208,     10-23. -   Kay, J. N., Voinescu, P. E., Chu, M. W., and Sanes, J. R. (2011).     Neurod6 expression defines new retinal amacrine cell subtypes and     regulates their fate. Nature neuroscience 14, 965-972. -   Kharchenko, P. V., Silberstein, L., and Scadden, D. T. (2014).     Bayesian approach to single-cell differential expression analysis.     Nature methods 11, 740-742. -   Kivioja, T., Vaharautio, A., Karlsson, K., Bonke, M., Enge, M.,     Linnarsson, S., and Taipale, J. (2012). Counting absolute numbers of     molecules using unique molecular identifiers. Nature methods 9,     72-74. -   Kurimoto, K., Yabuta, Y., Ohinata, Y., Ono, Y., Uno, K. D.,     Yamada, R. G., Ueda, H. R., and Saitou, M. (2006). An improved     single-cell cDNA amplification method for efficient high-density     oligonucleotide microarray analysis. Nucleic acids research 34, e42. -   Lareu, R. R., Harve, K. S., and Raghunath, M. (2007). Emulating a     crowded intracellular environment in vitro dramatically improves     RT-PCR performance. Biochemical and biophysical research     communications 363, 171-177. -   Leek, J. T., and Storey, J. D. (2011). The joint null criterion for     multiple hypothesis tests. Applications in Genetics and Molecular     Biology 10, 1-22. -   Luo, L., Callaway, E. M., and Svoboda, K. (2008). Genetic dissection     of neural circuits. Neuron 57, 634-660. -   Mao, C. A., Li, H., Zhang, Z., Kiyama, T., Panda, S., Hattar, S.,     Ribelayga, C. P., Mills, S. L., and Wang, S. W. (2014). T-box     transcription regulator Tbr2 is essential for the formation and     maintenance of Opn4/melanopsin-expressing intrinsically     photosensitive retinal ganglion cells. The Journal of neuroscience:     the official journal of the Society for Neuroscience 34,     13083-13095. -   Masland, R. H. (2012). The neuronal organization of the retina.     Neuron 76, 266-280. -   Masland, R. H., and Sanes, J. R. (2015). Retinal ganglion cell     types: Current states and lessons for the brain. Ann Rev Neurosci in     press. -   Mazutis, L., Gilbert, J., Ung, W. L., Weitz, D. A., Griffiths, A.     D., and Heyman, J. A. (2013). Single-cell analysis and sorting using     droplet-based microfluidics. Nature protocols 8, 870-891. -   McCarroll, S. A., Feng, G., and Hyman, S. E. (2014). Genome-scale     neurogenetics: methodology and meaning. Nature neuroscience 17,     756-763. -   McDavid, A., Finak, G., Chattopadyay, P. K., Dominguez, M.,     Lamoreaux, L., Ma, S. S., Roederer, M., and Gottardo, R. (2013).     Data exploration, quality control and testing in single-cell     qPCR-based gene expression experiments. Bioinformatics 29, 461-467. -   McDonald, J. C., Duffy, D. C., Anderson, J. R., Chiu, D. T., Wu, H.,     Schueller, O. J., and Whitesides, G. M. (2000). Fabrication of     microfluidic systems in poly(dimethylsiloxane). Electrophoresis 21,     27-40. -   Mills, S. L., O'Brien, J. J., Li, W., O'Brien, J., and Massey, S. C.     (2001). Rod pathways in the mammalian retina use connexin 36. The     Journal of comparative neurology 436, 336-350. -   Peres-Neto, P. R., Jackson, D. A., and Somers, K. M. (2005). How     many principal components? stopping rules for determining the number     of non-trivial axes revisited. Computational Statistics and Data     Analysis 49, 974-997. -   Petilla Interneuron Nomenclature, G., Ascoli, G. A.,     Alonso-Nanclares, L., Anderson, S. A., Barrionuevo, G.,     Benavides-Piccione, R., Burkhalter, A., Buzsaki, G., Cauli, B.,     Defelipe, J., et al. (2008). Petilla terminology: nomenclature of     features of GABAergic interneurons of the cerebral cortex. Nature     reviews Neuroscience 9, 557-568. -   Picelli, S., Bjorklund, A. K., Faridani, O R., Sagasser, S.,     Winberg, G., and Sandberg, R. (2013). Smart-seq2 for sensitive     full-length transcriptome profiling in single cells. Nature methods     10, 1096-1098. -   Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A.     A., Lui, J. H., Li, N., Szpankowski, L., Fowler, B., Chen, P., et     al. (2014). Low-coverage single-cell mRNA sequencing reveals     cellular heterogeneity and activated signaling pathways in     developing cerebral cortex. Nature biotechnology. -   Provis, J. M., Diaz, C. M., and Penfold, P. L. (1996). Microglia in     human retina: a heterogeneous population with distinct ontogenies.     Perspectives on developmental neurobiology 3, 213-222. -   Roberts, M. R., Srinivas, M., Forrest, D., Morreale de Escobar, G.,     and Reh, T. A. (2006). Making the gradient: thyroid hormone     regulates cone opsin expression in the developing mouse retina.     Proceedings of the National Academy of Sciences of the United States     of America 103, 6218-6223. -   Sanes, J. R., and Zipursky, S. L. (2010). Design principles of     insect and vertebrate visual systems. Neuron 66, 15-36. -   Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S.,     Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N.,     Malboeuf, C., Lu, D., et al. (2013). Single-cell transcriptomics     reveals bimodality in expression and splicing in immune cells.     Nature 498, 236-240. -   Shalek, A. K., Satija, R., Shuga, J., Trombetta, J. J., Gennert, D.,     Lu, D., Chen, P., Gertner, R. S., Gaublomme, J. T., Yosef, N., et     al. (2014). Single-cell RNA-seq reveals dynamic paracrine control of     cellular variation. Nature 510, 363-369. -   Shekhar, K., Brodin, P., Davis, M. M., and Chakraborty, A. K.     (2014). Automatic Classification of Cellular Expression by Nonlinear     Stochastic Embedding (ACCENSE). Proceedings of the National Academy     of Sciences of the United States of America 111, 202-207. -   Siegert, S., Cabuy, E., Scherf, B. G., Kohler, H., Panda, S., Le, Y.     Z., Fehling, H. J., Gaidatzis, D., Stadler, M. B., and Roska, B.     (2012). Transcriptional code and disease map for adult retinal cell     types. Nature neuroscience 15, 487-495, S481-482. -   Srivastava, S. C., Pandey, D., Srivastava, N. P., and Bajpai, S. P.     (2008). RNA Synthesis: phosphoramidites for RNA synthesis in the     reverse direction. Highly efficient synthesis and application to     convenient introduction of ligands, chromophores and modifications     of synthetic RNA at the 3′-end. Nucleic acids symposium series,     103-104. -   Starckx, S., Van den Steen, P. E., Verbeek, R., van Noort, J. M.,     and Opdenakker, G. (2003). A novel rationale for inhibition of     gelatinase B in multiple sclerosis: MMP-9 destroys alpha     B-crystallin and generates a promiscuous T cell epitope. Journal of     neuroimmunology 141, 47-57. -   Szel, A., Lukats, A., Fekete, T., Szepessy, Z., and Rohlich, P.     (2000). Photoreceptor distribution in the retinas of subprimate     mammals. Journal of the Optical Society of America A, Optics, image     science, and vision 17, 568-579. -   Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N.,     Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009).     mRNA-Seq whole-transcriptome analysis of a single cell. Nature     methods 6, 377-382. -   Thorsen, T., Roberts, R. W., Arnold, F. H., and Quake, S. R. (2001).     Dynamic pattern formation in a vesicle-generating microfluidic     device. Physical review letters 86, 4163-4166. -   Umbanhowar, P. B. P., V.; Weitz, D. A. (2000). Monodisperse Emulsion     Generation via Drop Break Off in a Coflowing Stream. Langmuir 16,     347-351. -   Utada, A. S., Fernandez-Nieves, A., Stone, H. A., and Weitz, D. A.     (2007). Dripping to jetting transitions in coflowing liquid streams.     Physical review letters 99, 094502. -   van der Maaten, L., and Hinton, G. (2008). Visualizing Data using     t-SNE. Journal of Machine Learning Research 9, 2579-2605. -   Vogelstein, B., and Kinzler, K. W. (1999). Digital PCR. Proceedings     of the National Academy of Sciences of the United States of America     96, 9236-9241. -   Wetmur, J. G., and Davidson, N. (1968). Kinetics of renaturation of     DNA. Journal of molecular biology 31, 349-370. -   White, A. K., VanInsberghe, M., Petriv, O. I., Hamidi, M., Sikorski,     D., Marra, M. A., Piret, J., Aparicio, S., and Hansen, C. L. (2011).     High-throughput microfluidic single-cell RT-qPCR. Proceedings of the     National Academy of Sciences of the United States of America 108,     13999-14004. -   Whitfield, M. L., Sherlock, G., Saldanha, A. J., Murray, J. I.,     Ball, C. A., Alexander, K. E., Matese, J. C., Perou, C. M., Hurt, M.     M., Brown, P. O., et al. (2002). Identification of genes     periodically expressed in the human cell cycle and their expression     in tumors. Molecular biology of the cell 13, 1977-2000. -   Yang, Y., and Cvekl, A. (2005). Tissue-specific regulation of the     mouse alphaA-crystallin gene in lens via recruitment of Pax6 and     c-Maf to its promoter. Journal of molecular biology 351, 453-469. -   Zhu, Y. Y., Machleder, E. M., Chenchik, A., Li, R., and     Siebert, P. D. (2001). Reverse transcriptase template switching: a     SMART approach for full-length cDNA library construction.     BioTechniques 30, 892-897.

The invention is further described by the following numbered paragraphs:

1. A nucleotide- or oligonucleotide-adorned bead wherein said bead comprises:

(a) a linker;

(b) an identical sequence for use as a sequencing priming site;

(c) a uniform or near-uniform nucleotide or oligonucleotide sequence;

(d) a Unique Molecular Identifier which differs for each priming site;

(e) optionally an oligonucleotide redundant sequence for capturing polyadenylated mRNAs and priming reverse transcription; and

(f) optionally at least one other oligonucleotide barcode which provides an additional substrate for identification.

2. The nucleotide- or oligonucleotide-adorned bead of paragraph 1 wherein the nucleotide or oligonucleotide sequence on the surface of the bead is a molecular barcode.

3. The nucleotide- or oligonucleotide-adorned bead of paragraph 2 wherein the barcode ranges from 4 to 1000 nucleotides in length.

4. The nucleotide- or oligonucleotide-adorned bead according to paragraph 1 wherein the oligonucleotide sequence for capturing polyadenylated mRNAs and priming reverse transcription is an oligo dT sequence.

5. The nucleotide- or oligonucleotide-adorned bead according to paragraph 1 wherein the linker is a non-cleavable, straight-chain polymer.

6. The nucleotide- or oligonucleotide-adorned bead according to paragraph 1 wherein the linker is a chemically-cleavable, straight-chain polymer.

7. The nucleotide- or oligonucleotide-adorned bead according to paragraph 1 wherein the linker is a non-cleavable optionally substituted hydrocarbon polymer.

8. The nucleotide- or oligonucleotide-adorned bead according to paragraph 1 wherein the linker is a photolabile optionally substituted hydrocarbon polymer.

9. The nucleotide- or oligonucleotide-adorned bead according to paragraph 1 wherein the linker is a polyethylene glycol.

10. The nucleotide- or oligonucleotide-adorned bead according to paragraph 1 wherein the linker is a PEG-C₃ to PEG-₂₄.

11. A mixture comprising a plurality of nucleotide- or oligonucleotide-adorned beads, wherein said beads comprises:

(a) a linker;

(b) an identical sequence for use as a sequencing priming site;

(c) a uniform or near-uniform nucleotide or oligonucleotide sequence;

(d) a Unique Molecular Identifier which differs for each priming site;

(e) an oligonucleotide redundant sequence for capturing polyadenylated mRNAs and priming reverse transcription; and

(f) optionally at least one additional oligonucleotide sequences, which provide substrates for downstream molecular-biological reactions;

wherein the uniform or near-uniform nucleotide or oligonucleotide sequence is the same across all the priming sites on any one bead, but varies among the oligonucleotides on an individual bead.

12. The mixture of paragraph 11 wherein the nucleotide or oligonucleotide sequence on the surface of the bead is a molecular barcode.

13. The mixture of paragraph 12 wherein the barcode ranges from 4 to 1000 nucleotides in length.

14. The mixture of paragraph 11 wherein the oligonucleotide sequence for capturing polyadenylated mRNAs and priming reverse transcription is an oligo dT sequence.

15. The mixture of paragraph 11 which comprises at least one oligonucleotide sequences, which provide for substrates for downstream molecular-biological reactions.

16. The mixture of paragraph 11 wherein the downstream molecular biological reactions are for reverse transcription of mature mRNAs; capturing specific portions of the transcriptome, priming for DNA polymerases and/or similar enzymes; or priming throughout the transcriptome or genome. 17. The mixture of paragraph 11 wherein the additional oligonucleotide sequence comprises a oligo-dT sequence. 18. The mixture of paragraph 11 wherein the additional oligonucleotide sequence comprises a primer sequence. 19. The mixture of paragraph 11 wherein the additional oligonucleotide sequence comprises a oligo-dT sequence and a primer sequence. 20. An error-correcting barcode bead wherein said bead comprises:

(a) a linker;

(b) an identical sequence for use as a sequencing priming site;

(c) a uniform or near-uniform nucleotide or oligonucleotide sequence which comprises at least a nucleotide base duplicate;

(d) a Unique Molecular Identifier which differs for each priming site; and

(e) an oligonucleotide redundant for capturing polyadenylated mRNAs and priming reverse transcription;

21. A method wherein the barcode beads of paragraph 20 fail to hybridize to the mRNA thereby failing to undergo reverse transcription.

22. A kit which comprises a mixture of oligonucleotide bound beads of paragraph 1 and self-correcting barcode beads of paragraph 20.

23. A method for creating a composite single-cell sequencing library comprising:

(a) merging one uniquely barcoded RNA capture microbead with a single-cell in an emulsion droplet having a diameter from 50 μm to 210 μm;

(b) lysing the cell thereby capturing the RNA on the RNA capture microbead;

(c) performing a reverse transcription reaction to convert the cells' RNA to first strand cDNA that is covalently linked to the RNA capture microbead; or conversely reverse transcribing within droplets and thereafter breaking droplets and collecting cDNA-attached beads;

(d) preparing and sequencing a single composite RNA-Seq library, containing cell barcodes that record the cell-of-origin of each RNA, and molecular barcodes that distinguish among RNAs from the same cell.

24. A method for creating a composite single-cell sequencing library comprising:

(a) merging one uniquely barcoded RNA capture microbead with a single-cell in an emulsion droplet having a diameter from 50 μm to 210 μm;

(b) lysing the cell thereby capturing the RNA on the RNA capture microbead;

(c) breaking droplets and pooling beads in solution;

(d) performing a reverse transcription reaction to convert the cells' RNA to first strand cDNA that is covalently linked to the RNA capture microbead; or conversely reverse transcribing within droplets and thereafter breaking droplets and collecting cDNA-attached beads;

(e) preparing and sequencing a single composite RNA-Seq library, containing cell barcodes that record the cell-of-origin of each RNA, and molecular barcodes that distinguish among RNAs from the same cell.

25. The method of paragraph 23 or paragraph 24, wherein the method of amplifying the cDNA-attached beads is template switch amplification.

26. The method of paragraph 23 or paragraph 24, wherein the method of amplifying the cDNA-attached beads is T7 linear application.

27. The method of paragraph 23 or paragraph 24, wherein the method of amplifying the cDNA-attached beads is exponential isothermal amplification.

28. The method of paragraph 23 or paragraph 24, wherein the emulsion droplet is formed via co-encapsulation comprising RNA capture microbead and composite single-cell.

29. The method of paragraph 25 wherein the emulsion droplet is at least 1.25 to times more than the volume of the RNA capture microbead.

30. The method of paragraph 29 wherein the emulsion droplet is at least 1.5 times the volume of the RNA capture microbead.

31. The method of paragraph 23 or paragraph 24, wherein the RNA is mRNA.

32. The method of paragraph 23 or paragraph 24 wherein the diameter of the emulsion droplet is 125 μm.

33. The method of paragraph 23 or paragraph 24 wherein the diameter of the RNA capture microbeads is from 10 μm to 95 μm.

34. A method for preparing a plurality of beads with unique nucleic acid sequence comprising:

(a) performing polynucleotide synthesis on the surface of the plurality of beads in a pool-and-split process, such that in each cycle of synthesis the beads are split into a plurality of subsets wherein each subset is subjected to different chemical reactions;

(b) repeating the pool-and-split process from anywhere from 2 cycles to 200 cycles.

35. The method of paragraph 34 wherein the polynucleotide synthesis is phosphoramidite synthesis.

36. The method of paragraph 34 wherein the polynucleotide synthesis is reverse direction phosphoramidite chemistry.

37. The method of paragraph 34 wherein each subset is subjected to a different nucleotide.

38. The method of paragraph 34 wherein each subset is subjected to a different canonical nucleotide.

39. The method of paragraph 34 is repeated three times.

40. The method of paragraph 34 is repeated four times.

41. The method of paragraph 34 is repeated twelve times.

42. The method of paragraph 34, wherein the linker covalently connecting the microbead to the oligonucleotide is polyethylene glycol.

43. The method of any one of paragraphs 34 through 42, wherein the diameter of the RNA capture microbeads is from 10 μm to 95 μm.

44. The method of any one of paragraphs 34 through 42 wherein multiple steps is twelve steps.

45. A method for simultaneously preparing a plurality of nucleotide- or oligonucleotide-adorned beads wherein a uniform, near-uniform, or patterned nucleotide or oligonucleotide sequence is synthesized upon any individual bead while vast numbers of different nucleotide or oligonucleotide sequences are simultaneously synthesized on different beads, comprising:

(a) forming a mixture comprising a plurality of beads;

(b) separating the beads into subsets;

(c) extending the nucleotide or oligonucleotide sequence on the surface of the beads by adding an individual nucleotide via chemical synthesis;

(d) pooling the subsets of beads in (c) into a single common pool;

(e) repeating steps (b), (c) and (d) multiple times to produce a combinatorially a thousand or more nucleotide or oligonucleotide sequences; and

(f) collecting the nucleotide- or oligonucleotide-adorned beads.

46. The method of paragraph 45 wherein the nucleotide or oligonucleotide sequence on the surface of the bead is a molecular barcode.

47. The method of paragraph 45 wherein the pool-and-split synthesis steps occur every 2-10 cycles, rather than every cycle.

48. The method of paragraph 45 wherein the barcode contains built-in error correction.

49. The method of paragraph 45 wherein the barcode ranges from 4 to 1000 nucleotides in length.

50. The method of paragraph 45 wherein the polynucleotide synthesis is phosphoramidite synthesis.

51. The method of paragraph 45 wherein the polynucleotide synthesis is reverse direction phosphoramidite chemistry.

52. The method of paragraph 45 wherein each subset is subjected to a different nucleotide.

53. The method of paragraph 45 further comprising wherein one or more subsets receive a cocktail of two nucleotides.

54. The method of paragraph 45 wherein each subset is subjected to a different canonical nucleotide.

55. The method of paragraph 45 wherein the bead is a microbead.

56. The method of paragraph 45 wherein the bead is a nanoparticle.

57. The method of paragraph 45 wherein the bead is a macrobead.

58. The method of paragraph 45 where the oligonucleotide sequence is a dinucleotide.

59. The method of paragraph 45 where the oligonucleotide sequence is a trinucleotide.

60. A method for simultaneously preparing a thousand or more nucleotide- or oligonucleotide-adorned beads wherein a uniform or near-uniform nucleotide or oligonucleotide sequence is synthesized upon any individual bead while a plurality of different nucleotide or oligonucleotide sequences are simultaneously synthesized on different beads, comprising:

(a) forming a mixture comprising a plurality of beads;

(b) separating the beads into subsets;

(c) extending the nucleotide or oligonucleotide sequence on the surface of the beads by adding an individual nucleotide via chemical synthesis;

(d) pooling the subsets of beads in (c) into a single common pool;

(e) repeating steps (b), (c) and (d) multiple times to produce a combinatorially large number of nucleotide or oligonucleotide sequences; and

(f) collecting the nucleotide- or oligonucleotide-adorned beads;

(g) performing polynucleotide synthesis on the surface of the plurality of beads in a pool-and-split synthesis, such that in each cycle of synthesis the beads are split into a plurality of subsets wherein each subset is subjected to different chemical reactions;

(h) repeating the pool-and-split synthesis multiple times.

61. The method of paragraph 60 wherein the nucleotide or oligonucleotide sequence on the surface of the bead is a molecular barcode.

62. The method of paragraph 60 wherein the pool-and-split synthesis steps occur every 2-10 cycles, rather than every cycle.

63. The method of paragraph 60 wherein the generated barcode contains built-in error correction.

64. The method of paragraph 60 wherein the barcode ranges from 4 to 1000 nucleotides in length.

65. The method of paragraph 60 wherein the polynucleotide synthesis is phosphoramidite synthesis.

66. The method of paragraph 60 wherein the polynucleotide synthesis is reverse direction phosphoramidite chemistry.

67. The method of paragraph 60 wherein each subset is subjected to a different nucleotide.

68. The method of paragraph 60 further comprising wherein one or more subsets receive a cocktail of two nucleotides.

69. The method of paragraph 60 wherein each subset is subjected to a different canonical nucleotide.

70. The method of paragraph 60 wherein the bead is a microbead.

71. The method of paragraph 60 wherein the bead is a nanoparticle.

72. The method of paragraph 60 wherein the bead is a macrobead.

73. The method of paragraph 60 where the oligonucleotide barcoded bead is a dinucleotide.

74. The method of paragraph 60 where the oligonucleotide barcoded bead is a trinucleotide.

75. The method of paragraph 45 or paragraph 60 wherein the pool-and-split synthesis is repeated twelve times.

76. The method of paragraph 45 or paragraph 60 wherein the diameter of the complexed bead is from 10 μm to 95 μm.

77. An apparatus for creating a composite single-cell sequencing library via a microfluidic system, comprising:

a oil-surfactant inlet comprising a filter and a carrier fluid channel, wherein said carrier fluid channel further comprises a resistor;

an inlet for an analyte comprising a filter and a carrier fluid channel, wherein said carrier fluid channel further comprises a resistor;

an inlet for mRNA capture microbeads and lysis reagent comprising a filter and a carrier fluid channel, wherein said carrier fluid channel further comprises a resistor;

said carrier fluid channels have a carrier fluid flowing therein at an adjustable or predetermined flow rate;

wherein each said carrier fluid channels merge at a junction; and said junction being connected to a mixer, which contains an outlet for drops.

78. The apparatus of paragraph 77, wherein the analyte comprises a chemical reagent, a protein, a drug, an antibody, an enzyme, a nucleic acid, an organelle, a cell or any combination thereof.

79. The apparatus of paragraph 77 wherein said junction is connected to said mixer by a fluid carrier channel with a constriction for droplet pinch-off.

80. The apparatus of paragraph 77, wherein the analyte is a cell.

81. The apparatus of paragraph 77, wherein the analyte is a mammalian cell.

82. The apparatus of paragraph 77, wherein the analyte is complex tissue.

83. The apparatus of paragraph 81, wherein the cell is a brain cell.

84. The apparatus of paragraph 81, wherein the cell is a retina cell.

85. The apparatus of paragraph 81, wherein the cell is a human bone marrow cell.

86. The apparatus of paragraph 81, wherein the cell is a host-pathogen cell.

87. The apparatus of paragraph 77, wherein the lysis reagent comprises an anionic surfactant, such as sodium lauroyl sarcosine, or a chaotropic salt, such as guanidinium thiocyanate.

88. The apparatus of paragraph 77, wherein the filter comprises square PDMS.

89. The apparatus of paragraph 77, wherein the resistor is serpentine having a length from 7000-9000, width of 50-75 μm and depth of 100-150 mm.

90. The resistor of paragraph 89, which has a diameter of 50 μm.

91. The apparatus of paragraph 77, wherein the channels having a length of length of 8000-12,000 μm and width of 125-250 mm, and depth of 100-150 mm.

92. The channel of paragraph 89, wherein the diameter is 125 μm.

93. The apparatus of paragraph 77, wherein the mixer has a length of 7000-9000 μm and a width of 110-140 μm.

94. The mixer of paragraph 93, wherein the width is 125 μm.

95. The apparatus of paragraph 77, wherein the oil-surfactant is a PEG block polymer.

96. The apparatus of paragraph 95, wherein the PEG block polymer is BIORAD™ QX200 Droplet Generation Oil.

97. The apparatus of paragraph 77, wherein the carrier fluid is water-glycerol mixture.

Having thus described in detail preferred embodiments of the present invention, it is to be understood that the invention defined by the above paragraphs is not to be limited to particular details set forth in the above description as many apparent variations thereof are possible without departing from the spirit or scope of the present invention. 

What is claimed is:
 1. A method for creating a composite single-cell cDNA library, wherein the RNAs from different cells are tagged individually, allowing the cell identity of each RNA to be retained in a single library, said method comprising: (a) loading an aqueous input comprising a plurality of single cells and an aqueous input comprising a plurality of at least a thousand RNA capture microbeads into a microfluidic device configured for joining the two aqueous inputs into a plurality of emulsion droplets, wherein each RNA capture microbead comprises a plurality of capture oligonucleotides attached to the microbead surface, each capture oligonucleotide comprising (i) a cell-of-origin barcode sequence that is the same for all capture oligonucleotides on the same bead but differs from the barcode sequence of capture oligonucleotides on other beads, (ii) a unique molecular identifier (UMI) sequence that is different for each capture oligonucleotide on the same microbead and (iii) a capture sequence that binds to cellular RNA, and wherein the maximum complexity of cell-of-origin barcodes for the plurality of RNA capture microbeads is 4^(n) where n is the length of the cell-of-origin barcode sequence and n is at least 6, and wherein the cell-of-origin barcode sequence is contiguous with the UMI sequence; (b) co-encapsulating single cells and single RNA capture microbeads in emulsion droplets by co-flowing the two aqueous inputs across an oil channel in the microfluidic device, and wherein each emulsion droplet has a diameter from 50 μm to 210 μm; (c) lysing the cells in the emulsion droplets, thereby capturing the cellular RNA on the RNA capture oligonucleotides; and (d) performing a reverse transcription reaction to generate cDNA copies of the captured RNA that incorporate the barcode sequence and a UMI, thereby recording the cell-of-origin for the captured RNA and identifying individual transcripts.
 2. The method of claim 1, further comprising amplifying the cDNA, wherein the method of amplifying the cDNA is template switch amplification; T7 linear application; exponential isothermal amplification; or PCR.
 3. The method of claim 1, further comprising preparing a massively parallel single cell RNA sequencing library from the cDNA and sequencing the sequencing library.
 4. The method of claim 1, further comprising collecting cDNA-attached beads.
 5. The method of claim 1, wherein the capture oligonucleotides are attached to the beads via a cleavable linker and the method comprises lysing the cells and releasing the capture oligonucleotides from the beads.
 6. The method of claim 1, wherein the emulsion droplet has a diameter of 125 μm.
 7. The method of claim 1, wherein the diameter of the RNA capture microbeads is from 10 μm to 95 μm.
 8. The method of claim 1, wherein the capture sequence is a poly-T (dT) sequence.
 9. The method of claim 1, wherein the capture oligonucleotide further comprises a common priming sequence which comprises the same sequence across all beads in the plurality of beads.
 10. The method of claim 1, wherein the capture oligonucleotide further comprises a second barcode sequence that comprises the same sequence across all capture oligonucleotides on a bead but differs from the first barcode sequence.
 11. The method of claim 1, wherein the capture oligonucleotide is a single-stranded oligonucleotide.
 12. The method of claim 1, wherein the plurality of capture oligonucleotides are attached to the surface of the microbead via one or more linkers.
 13. The method of claim 12 wherein the linker is a non-cleavable straight-chain polymer or substituted hydrocarbon polymer.
 14. The method of claim 12, wherein the linker is a chemically-cleavable or photolabile linker.
 15. The method of claim 1, wherein the reverse transcription reaction is performed in the droplets.
 16. The method of claim 1, wherein the reverse transcription reaction is performed after releasing the microbeads from the droplets.
 17. The method of claim 1, wherein the bead material is porous.
 18. The method of claim 17, wherein the bead material is methacrylate resin.
 19. The method of claim 1, wherein the plurality of RNA capture microbeads comprise 100,000 to 10 million uniquely barcoded RNA capture microbeads.
 20. The method of claim 1, wherein the cell-of-origin barcode is 6-12 nucleotides.
 21. The method of claim 1, wherein the capture oligonucleotide comprises at least one chemically modified nucleotide. 